diff --git a/README.rst b/README.rst
index 3f9a4ad726806..4ac297063c26e 100644
--- a/README.rst
+++ b/README.rst
@@ -36,7 +36,7 @@
 .. |NumPyMinVersion| replace:: 1.19.5
 .. |SciPyMinVersion| replace:: 1.6.0
 .. |JoblibMinVersion| replace:: 1.2.0
-.. |ThreadpoolctlMinVersion| replace:: 2.0.0
+.. |ThreadpoolctlMinVersion| replace:: 3.1.0
 .. |MatplotlibMinVersion| replace:: 3.3.4
 .. |Scikit-ImageMinVersion| replace:: 0.17.2
 .. |PandasMinVersion| replace:: 1.1.5
diff --git a/build_tools/azure/debian_atlas_32bit_lock.txt b/build_tools/azure/debian_atlas_32bit_lock.txt
index a6bb0485d1933..5bff365bb1eea 100644
--- a/build_tools/azure/debian_atlas_32bit_lock.txt
+++ b/build_tools/azure/debian_atlas_32bit_lock.txt
@@ -37,7 +37,7 @@ pytest==7.1.2
     #   pytest-cov
 pytest-cov==2.9.0
     # via -r build_tools/azure/debian_atlas_32bit_requirements.txt
-threadpoolctl==2.2.0
+threadpoolctl==3.1.0
     # via -r build_tools/azure/debian_atlas_32bit_requirements.txt
 tomli==2.0.1
     # via
diff --git a/build_tools/azure/debian_atlas_32bit_requirements.txt b/build_tools/azure/debian_atlas_32bit_requirements.txt
index d1bc22529d4f4..615193a71fc6b 100644
--- a/build_tools/azure/debian_atlas_32bit_requirements.txt
+++ b/build_tools/azure/debian_atlas_32bit_requirements.txt
@@ -3,7 +3,7 @@
 # build_tools/update_environments_and_lock_files.py
 cython==3.0.10  # min
 joblib==1.2.0  # min
-threadpoolctl==2.2.0
+threadpoolctl==3.1.0
 pytest==7.1.2  # min
 pytest-cov==2.9.0  # min
 ninja
diff --git a/build_tools/azure/pylatest_conda_mkl_no_openmp_environment.yml b/build_tools/azure/pylatest_conda_mkl_no_openmp_environment.yml
index 9c46400c2d3c6..01bd378aa121a 100644
--- a/build_tools/azure/pylatest_conda_mkl_no_openmp_environment.yml
+++ b/build_tools/azure/pylatest_conda_mkl_no_openmp_environment.yml
@@ -9,7 +9,6 @@ dependencies:
   - blas[build=mkl]
   - scipy<1.12
   - joblib
-  - threadpoolctl
   - matplotlib
   - pandas
   - pyamg
@@ -25,3 +24,4 @@ dependencies:
   - pip
   - pip:
     - cython
+    - threadpoolctl
diff --git a/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock b/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock
index a9c9b364426a7..7da0e513ac143 100644
--- a/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock
+++ b/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock
@@ -1,6 +1,6 @@
 # Generated by conda-lock.
 # platform: osx-64
-# input_hash: d3fadab6d5d5d715660beb53286e2687b018b5195ef7ce47928bb79a96ee851b
+# input_hash: e0d2cf2593df1f2c6969d68cf849136bee785b51f6cfc50ea1bdca2143d4a051
 @EXPLICIT
 https://repo.anaconda.com/pkgs/main/osx-64/blas-1.0-mkl.conda#cb2c87e85ac8e0ceae776d26d4214c8a
 https://repo.anaconda.com/pkgs/main/osx-64/bzip2-1.0.8-h6c40b1e_5.conda#0f51dde96c82dcf58a788787fed4c5b9
@@ -56,7 +56,6 @@ https://repo.anaconda.com/pkgs/main/noarch/python-tzdata-2023.3-pyhd3eb1b0_0.con
 https://repo.anaconda.com/pkgs/main/osx-64/pytz-2023.3.post1-py312hecd8cb5_0.conda#2636382c9a424f69cbc36b1c5dc1f2fc
 https://repo.anaconda.com/pkgs/main/osx-64/setuptools-68.2.2-py312hecd8cb5_0.conda#64235f0c451427d86808c70c1c31cb8b
 https://repo.anaconda.com/pkgs/main/noarch/six-1.16.0-pyhd3eb1b0_1.conda#34586824d411d36af2fa40e799c172d0
-https://repo.anaconda.com/pkgs/main/noarch/threadpoolctl-2.2.0-pyh0d69192_0.conda#bbfdbae4934150b902f97daaf287efe2
 https://repo.anaconda.com/pkgs/main/noarch/toml-0.10.2-pyhd3eb1b0_0.conda#cda05f5f6d8509529d1a2743288d197a
 https://repo.anaconda.com/pkgs/main/osx-64/tornado-6.3.3-py312h6c40b1e_0.conda#49173b5a36c9134865221f29d4a73fb6
 https://repo.anaconda.com/pkgs/main/osx-64/unicodedata2-15.1.0-py312h6c40b1e_0.conda#65bd2cb787fc99662d9bb6e6520c5826
@@ -84,3 +83,4 @@ https://repo.anaconda.com/pkgs/main/osx-64/scipy-1.11.4-py312h81688c2_0.conda#7d
 https://repo.anaconda.com/pkgs/main/osx-64/pandas-2.2.1-py312he282a81_0.conda#021b70a1e40efb75b89eb8ebdb347132
 https://repo.anaconda.com/pkgs/main/osx-64/pyamg-4.2.3-py312h44cbcf4_0.conda#3bdc7be74087b3a5a83c520a74e1e8eb
 # pip cython @ https://files.pythonhosted.org/packages/d5/6d/06c08d75adb98cdf72af18801e193d22580cc86ca553610f430f18ea26b3/Cython-3.0.10-cp312-cp312-macosx_10_9_x86_64.whl#sha256=8f2864ab5fcd27a346f0b50f901ebeb8f60b25a60a575ccfd982e7f3e9674914
+# pip threadpoolctl @ https://files.pythonhosted.org/packages/1e/84/ccd9b08653022b7785b6e3ee070ffb2825841e0dc119be22f0840b2b35cb/threadpoolctl-3.4.0-py3-none-any.whl#sha256=8f4c689a65b23e5ed825c8436a92b818aac005e0f3715f6a1664d7c7ee29d262
diff --git a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock
index b5f1d7613fa72..a4d18dc37716a 100644
--- a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock
+++ b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock
@@ -11,7 +11,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/libstdcxx-ng-11.2.0-h1234567_1.cond
 https://repo.anaconda.com/pkgs/main/linux-64/_openmp_mutex-5.1-1_gnu.conda#71d281e9c2192cb3fa425655a8defb85
 https://repo.anaconda.com/pkgs/main/linux-64/libgcc-ng-11.2.0-h1234567_1.conda#a87728dabf3151fb9cfa990bd2eb0464
 https://repo.anaconda.com/pkgs/main/linux-64/bzip2-1.0.8-h5eee18b_5.conda#9c8dec113089c4aca7392c6a3864f505
-https://repo.anaconda.com/pkgs/main/linux-64/expat-2.5.0-h6a678d5_0.conda#9a21d99d49a0a556cf9590430dec8ec0
+https://repo.anaconda.com/pkgs/main/linux-64/expat-2.6.2-h6a678d5_0.conda#55049db2772dae035f6b8a95f72b5970
 https://repo.anaconda.com/pkgs/main/linux-64/libffi-3.4.4-h6a678d5_0.conda#06e288f9250abef59b9a367d151fc339
 https://repo.anaconda.com/pkgs/main/linux-64/libuuid-1.41.5-h5eee18b_0.conda#4a6a2354414c9080327274aa514e5299
 https://repo.anaconda.com/pkgs/main/linux-64/ncurses-6.4-h6a678d5_0.conda#5558eec6e2191741a92f832ea826251c
@@ -22,7 +22,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/ccache-3.7.9-hfe4627d_0.conda#bef6f
 https://repo.anaconda.com/pkgs/main/linux-64/readline-8.2-h5eee18b_0.conda#be42180685cce6e6b0329201d9f48efb
 https://repo.anaconda.com/pkgs/main/linux-64/tk-8.6.12-h1ccaba5_0.conda#fa10ff4aa631fa4aa090a6234d7770b9
 https://repo.anaconda.com/pkgs/main/linux-64/sqlite-3.41.2-h5eee18b_0.conda#c7086c9ceb6cfe1c4c729a774a2d88a5
-https://repo.anaconda.com/pkgs/main/linux-64/python-3.12.2-h996f2a0_0.conda#bc4748d0d26253c8499a3abeca289469
+https://repo.anaconda.com/pkgs/main/linux-64/python-3.12.3-h996f2a0_0.conda#77af2bd351a8311d1e780bcfa7819bb8
 https://repo.anaconda.com/pkgs/main/linux-64/setuptools-68.2.2-py312h06a4308_0.conda#83ba634cde4f30d9e0b88e4ac9716ca4
 https://repo.anaconda.com/pkgs/main/linux-64/wheel-0.41.2-py312h06a4308_0.conda#b2c4f82880d58d679f3982370d80c0e2
 https://repo.anaconda.com/pkgs/main/linux-64/pip-23.3.1-py312h06a4308_0.conda#e1d44bca4a257e84af33503233491107
@@ -31,7 +31,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-23.3.1-py312h06a4308_0.conda#e1
 # pip certifi @ https://files.pythonhosted.org/packages/ba/06/a07f096c664aeb9f01624f858c3add0a4e913d6c96257acb4fce61e7de14/certifi-2024.2.2-py3-none-any.whl#sha256=dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1
 # pip charset-normalizer @ https://files.pythonhosted.org/packages/ee/fb/14d30eb4956408ee3ae09ad34299131fb383c47df355ddb428a7331cfa1e/charset_normalizer-3.3.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=90d558489962fd4918143277a773316e56c72da56ec7aa3dc3dbbe20fdfed15b
 # pip coverage @ https://files.pythonhosted.org/packages/98/79/185cb42910b6a2b2851980407c8445ac0da0750dff65e420e86f973c8396/coverage-7.4.4-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=ce4b94265ca988c3f8e479e741693d143026632672e3ff924f25fab50518dd51
-# pip docutils @ https://files.pythonhosted.org/packages/26/87/f238c0670b94533ac0353a4e2a1a771a0cc73277b88bff23d3ae35a256c1/docutils-0.20.1-py3-none-any.whl#sha256=96f387a2c5562db4476f09f13bbab2192e764cac08ebbf3a34a95d9b1e4a59d6
+# pip docutils @ https://files.pythonhosted.org/packages/ea/6a/1c934b70bc12be40be886060709317c2ad5a5fe2180469410bd9344f5235/docutils-0.21.1-py3-none-any.whl#sha256=14c8d34a55b46c88f9f714adb29cefbdd69fb82f3fef825e59c5faab935390d8
 # pip execnet @ https://files.pythonhosted.org/packages/43/09/2aea36ff60d16dd8879bdb2f5b3ee0ba8d08cbbdcdfe870e695ce3784385/execnet-2.1.1-py3-none-any.whl#sha256=26dee51f1b80cebd6d0ca8e74dd8745419761d3bef34163928cbebbdc4749fdc
 # pip idna @ https://files.pythonhosted.org/packages/e5/3e/741d8c82801c347547f8a2a06aa57dbb1992be9e948df2ea0eda2c8b79e8/idna-3.7-py3-none-any.whl#sha256=82fee1fc78add43492d3a1898bfa6d8a904cc97d8427f683ed8e798d07761aa0
 # pip imagesize @ https://files.pythonhosted.org/packages/ff/62/85c4c919272577931d407be5ba5d71c20f0b616d31a0befe0ae45bb79abd/imagesize-1.4.1-py2.py3-none-any.whl#sha256=0d8d18d08f840c19d0ee7ca1fd82490fdc3729b7ac93f49870406ddde8ef8d8b
@@ -41,7 +41,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-23.3.1-py312h06a4308_0.conda#e1
 # pip ninja @ https://files.pythonhosted.org/packages/6d/92/8d7aebd4430ab5ff65df2bfee6d5745f95c004284db2d8ca76dcbfd9de47/ninja-1.11.1.1-py2.py3-none-manylinux1_x86_64.manylinux_2_5_x86_64.whl#sha256=84502ec98f02a037a169c4b0d5d86075eaf6afc55e1879003d6cab51ced2ea4b
 # pip packaging @ https://files.pythonhosted.org/packages/49/df/1fceb2f8900f8639e278b056416d49134fb8d84c5942ffaa01ad34782422/packaging-24.0-py3-none-any.whl#sha256=2ddfb553fdf02fb784c234c7ba6ccc288296ceabec964ad2eae3777778130bc5
 # pip platformdirs @ https://files.pythonhosted.org/packages/55/72/4898c44ee9ea6f43396fbc23d9bfaf3d06e01b83698bdf2e4c919deceb7c/platformdirs-4.2.0-py3-none-any.whl#sha256=0614df2a2f37e1a662acbd8e2b25b92ccf8632929bc6d43467e17fe89c75e068
-# pip pluggy @ https://files.pythonhosted.org/packages/a5/5b/0cc789b59e8cc1bf288b38111d002d8c5917123194d45b29dcdac64723cc/pluggy-1.4.0-py3-none-any.whl#sha256=7db9f7b503d67d1c5b95f59773ebb58a8c1c288129a88665838012cfb07b8981
+# pip pluggy @ https://files.pythonhosted.org/packages/88/5f/e351af9a41f866ac3f1fac4ca0613908d9a41741cfcf2228f4ad853b697d/pluggy-1.5.0-py3-none-any.whl#sha256=44e1ad92c8ca002de6377e165f3e0f1be63266ab4d554740532335b9d75ea669
 # pip pygments @ https://files.pythonhosted.org/packages/97/9c/372fef8377a6e340b1704768d20daaded98bf13282b5327beb2e2fe2c7ef/pygments-2.17.2-py3-none-any.whl#sha256=b27c2826c47d0f3219f29554824c30c5e8945175d888647acd804ddd04af846c
 # pip six @ https://files.pythonhosted.org/packages/d9/5a/e7c31adbe875f2abbb91bd84cf2dc52d792b5a01506781dbcf25c91daf11/six-1.16.0-py2.py3-none-any.whl#sha256=8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254
 # pip snowballstemmer @ https://files.pythonhosted.org/packages/ed/dc/c02e01294f7265e63a7315fe086dd1df7dacb9f840a804da846b96d01b96/snowballstemmer-2.2.0-py2.py3-none-any.whl#sha256=c8e1716e83cc398ae16824e5572ae04e0d9fc2c6b985fb0f900f5f0c96ecba1a
@@ -55,13 +55,13 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-23.3.1-py312h06a4308_0.conda#e1
 # pip threadpoolctl @ https://files.pythonhosted.org/packages/1e/84/ccd9b08653022b7785b6e3ee070ffb2825841e0dc119be22f0840b2b35cb/threadpoolctl-3.4.0-py3-none-any.whl#sha256=8f4c689a65b23e5ed825c8436a92b818aac005e0f3715f6a1664d7c7ee29d262
 # pip urllib3 @ https://files.pythonhosted.org/packages/a2/73/a68704750a7679d0b6d3ad7aa8d4da8e14e151ae82e6fee774e6e0d05ec8/urllib3-2.2.1-py3-none-any.whl#sha256=450b20ec296a467077128bff42b73080516e71b56ff59a60a02bef2232c4fa9d
 # pip jinja2 @ https://files.pythonhosted.org/packages/30/6d/6de6be2d02603ab56e72997708809e8a5b0fbfee080735109b40a3564843/Jinja2-3.1.3-py3-none-any.whl#sha256=7d6d50dd97d52cbc355597bd845fabfbac3f551e1f99619e39a35ce8c370b5fa
-# pip pyproject-metadata @ https://files.pythonhosted.org/packages/c4/cb/4678dfd70cd2f2d8969e571cdc1bb1e9293c698f8d1cf428fadcf48d6e9f/pyproject_metadata-0.7.1-py3-none-any.whl#sha256=28691fbb36266a819ec56c9fa1ecaf36f879d6944dfde5411e87fc4ff793aa60
+# pip pyproject-metadata @ https://files.pythonhosted.org/packages/aa/5f/bb5970d3d04173b46c9037109f7f05fc8904ff5be073ee49bb6ff00301bc/pyproject_metadata-0.8.0-py3-none-any.whl#sha256=ad858d448e1d3a1fb408ac5bac9ea7743e7a8bbb472f2693aaa334d2db42f526
 # pip pytest @ https://files.pythonhosted.org/packages/51/ff/f6e8b8f39e08547faece4bd80f89d5a8de68a38b2d179cc1c4490ffa3286/pytest-7.4.4-py3-none-any.whl#sha256=b090cdf5ed60bf4c45261be03239c2c1c22df034fbffe691abe93cd80cea01d8
 # pip python-dateutil @ https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl#sha256=a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427
 # pip requests @ https://files.pythonhosted.org/packages/70/8e/0e2d847013cb52cd35b38c009bb167a1a26b2ce6cd6965bf26b47bc0bf44/requests-2.31.0-py3-none-any.whl#sha256=58cd2187c01e70e6e26505bca751777aa9f2ee0b7f4300988b709f44e013003f
-# pip meson-python @ https://files.pythonhosted.org/packages/1f/60/b10b11ab470a690d5777310d6cfd1c9bdbbb0a1313a78c34a1e82e0b9d27/meson_python-0.15.0-py3-none-any.whl#sha256=3ae38253ff02b2e947a05e362a2eaf5a9a09d133c5666b4123399ee5fbf2e591
+# pip meson-python @ https://files.pythonhosted.org/packages/91/c0/104cb6244c83fe6bc3886f144cc433db0c0c78efac5dc00e409a5a08c87d/meson_python-0.16.0-py3-none-any.whl#sha256=842dc9f5dc29e55fc769ff1b6fe328412fe6c870220fc321060a1d2d395e69e8
 # pip pooch @ https://files.pythonhosted.org/packages/f4/72/8ae0f1ba4ce6a4f6d4d01a60a9fdf690fde188c45c1872b0b4ddb0607ace/pooch-1.8.1-py3-none-any.whl#sha256=6b56611ac320c239faece1ac51a60b25796792599ce5c0b1bb87bf01df55e0a9
 # pip pytest-cov @ https://files.pythonhosted.org/packages/78/3a/af5b4fa5961d9a1e6237b530eb87dd04aea6eb83da09d2a4073d81b54ccf/pytest_cov-5.0.0-py3-none-any.whl#sha256=4f0764a1219df53214206bf1feea4633c3b558a2925c8b59f144f682861ce652
-# pip pytest-xdist @ https://files.pythonhosted.org/packages/50/37/125fe5ec459321e2d48a0c38672cfc2419ad87d580196fd894e5f25230b0/pytest_xdist-3.5.0-py3-none-any.whl#sha256=d075629c7e00b611df89f490a5063944bee7a4362a5ff11c7cc7824a03dfce24
-# pip sphinx @ https://files.pythonhosted.org/packages/b2/b6/8ed35256aa530a9d3da15d20bdc0ba888d5364441bb50a5a83ee7827affe/sphinx-7.2.6-py3-none-any.whl#sha256=1e09160a40b956dc623c910118fa636da93bd3ca0b9876a7b3df90f07d691560
+# pip pytest-xdist @ https://files.pythonhosted.org/packages/e0/df/585742c204227526baf641e45a95d3c0a94978b85a1414622c1017ebfcf8/pytest_xdist-3.6.0-py3-none-any.whl#sha256=958e08f38472e1b3a83450d8d3e682e90fdbffee39a97dd0f27185a3bd9074d1
+# pip sphinx @ https://files.pythonhosted.org/packages/b4/fa/130c32ed94cf270e3d0b9ded16fb7b2c8fea86fa7263c29a696a30c1dde7/sphinx-7.3.7-py3-none-any.whl#sha256=413f75440be4cacf328f580b4274ada4565fb2187d696a84970c23f77b64d8c3
 # pip numpydoc @ https://files.pythonhosted.org/packages/f0/fa/dcfe0f65660661db757ee9ebd84e170ff98edd5d80235f62457d9088f85f/numpydoc-1.7.0-py3-none-any.whl#sha256=5a56419d931310d79a06cfc2a126d1558700feeb9b4f3d8dcae1a8134be829c9
diff --git a/build_tools/azure/pymin_conda_defaults_openblas_environment.yml b/build_tools/azure/pymin_conda_defaults_openblas_environment.yml
index a422a0a539c53..17824c9b97074 100644
--- a/build_tools/azure/pymin_conda_defaults_openblas_environment.yml
+++ b/build_tools/azure/pymin_conda_defaults_openblas_environment.yml
@@ -9,7 +9,6 @@ dependencies:
   - blas[build=openblas]
   - scipy=1.7
   - joblib
-  - threadpoolctl=2.2.0
   - matplotlib=3.3.4  # min
   - pyamg
   - pytest<8
@@ -21,3 +20,4 @@ dependencies:
   - pip
   - pip:
     - cython==3.0.10  # min
+    - threadpoolctl
diff --git a/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock b/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock
index 00797394e4253..fef1871be2088 100644
--- a/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock
+++ b/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock
@@ -1,6 +1,6 @@
 # Generated by conda-lock.
 # platform: linux-64
-# input_hash: 3d6bcb087065974114c1567c8dccd947a7376a7503b3514d82904299b651692d
+# input_hash: 4f71ddcce93c9279161f5b016be417469aa5df726d06e4a1447c9270f60179e4
 @EXPLICIT
 https://repo.anaconda.com/pkgs/main/linux-64/_libgcc_mutex-0.1-main.conda#c3473ff8bdb3d124ed5ff11ec380d6f9
 https://repo.anaconda.com/pkgs/main/linux-64/blas-1.0-openblas.conda#9ddfcaef10d79366c90128f5dc444be8
@@ -74,7 +74,6 @@ https://repo.anaconda.com/pkgs/main/linux-64/pyparsing-3.0.9-py39h06a4308_0.cond
 https://repo.anaconda.com/pkgs/main/linux-64/pyqt5-sip-12.13.0-py39h5eee18b_0.conda#256840c3841b52346ea5743be8490ede
 https://repo.anaconda.com/pkgs/main/linux-64/setuptools-68.2.2-py39h06a4308_0.conda#5b42cae5548732ae5c167bb1066085de
 https://repo.anaconda.com/pkgs/main/noarch/six-1.16.0-pyhd3eb1b0_1.conda#34586824d411d36af2fa40e799c172d0
-https://repo.anaconda.com/pkgs/main/noarch/threadpoolctl-2.2.0-pyh0d69192_0.conda#bbfdbae4934150b902f97daaf287efe2
 https://repo.anaconda.com/pkgs/main/noarch/toml-0.10.2-pyhd3eb1b0_0.conda#cda05f5f6d8509529d1a2743288d197a
 https://repo.anaconda.com/pkgs/main/linux-64/tomli-2.0.1-py39h06a4308_0.conda#b06dffe7ddca2645ed72f5116f0a087d
 https://repo.anaconda.com/pkgs/main/linux-64/tornado-6.3.3-py39h5eee18b_0.conda#9c4bd985bb8adcd12f47e790e95a9333
@@ -97,3 +96,4 @@ https://repo.anaconda.com/pkgs/main/linux-64/qt-main-5.15.2-h53bd1ea_10.conda#bd
 https://repo.anaconda.com/pkgs/main/linux-64/pyqt-5.15.10-py39h6a678d5_0.conda#52da5ff9b1144b078d2f41bab0b213f2
 https://repo.anaconda.com/pkgs/main/linux-64/matplotlib-3.3.4-py39h06a4308_0.conda#384fc5e01ebfcf30e7161119d3029b5a
 # pip cython @ https://files.pythonhosted.org/packages/a7/f5/3dde4d96076888ceaa981827b098274c2b45ddd4b20d75a8cfaa92b91eec/Cython-3.0.10-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=651a15a8534ebfb9b58cb0b87c269c70984b6f9c88bfe65e4f635f0e3f07dfcd
+# pip threadpoolctl @ https://files.pythonhosted.org/packages/1e/84/ccd9b08653022b7785b6e3ee070ffb2825841e0dc119be22f0840b2b35cb/threadpoolctl-3.4.0-py3-none-any.whl#sha256=8f4c689a65b23e5ed825c8436a92b818aac005e0f3715f6a1664d7c7ee29d262
diff --git a/build_tools/azure/pypy3_linux-64_conda.lock b/build_tools/azure/pypy3_linux-64_conda.lock
index 23710cfe35cb8..342bd1af08406 100644
--- a/build_tools/azure/pypy3_linux-64_conda.lock
+++ b/build_tools/azure/pypy3_linux-64_conda.lock
@@ -4,23 +4,23 @@
 @EXPLICIT
 https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81
 https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.2.2-hbcca054_0.conda#2f4327a1cbe7f022401b236e915a5fef
-https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-13.2.0-h7e041cc_5.conda#f6f6600d18a4047b54f803cf708b868a
+https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-13.2.0-h95c4c6d_6.conda#3cfab3e709f77e9f1b3d380eb622494a
 https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.9-4_pypy39_pp73.conda#c1b2f29111681a4036ed21eaa3f44620
 https://conda.anaconda.org/conda-forge/noarch/tzdata-2024a-h0c530f3_0.conda#161081fc7cec0bfda0d86d7cb595f8d8
 https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_kmp_llvm.tar.bz2#562b26ba2e19059551a811e72ab7f793
-https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-13.2.0-h807b86a_5.conda#d4ff227c46917d3b4565302a2bbb276b
+https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-13.2.0-hc881cc4_6.conda#df88796bd09a0d2ed292e59101478ad8
 https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hd590300_5.conda#69b8b6202a07720f448be700e300ccf4
 https://conda.anaconda.org/conda-forge/linux-64/lerc-4.0.0-h27087fc_0.tar.bz2#76bbff344f0134279f225174e9064c8f
 https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.1.0-hd590300_1.conda#aec6c91c7371c26392a06708a73c70e5
 https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.20-hd590300_0.conda#8e88f9389f1165d7c0936fe40d9a9a79
 https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.6.2-h59595ed_0.conda#e7ba12deb7020dd080c6c70e7b6f6a3d
 https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.2-h7f98852_5.tar.bz2#d645c6d2ac96843a2bfaccd2d62b3ac3
-https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-13.2.0-ha4646dd_5.conda#7a6bd7a12a4bd359e2afe6c0fa1acace
+https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-13.2.0-h43f5ff8_6.conda#e54a5ddc67e673f9105cf2a2e9c070b0
 https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.0.0-hd590300_1.conda#ea25936bb4080d843790b586850f82b8
-https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.3.2-hd590300_1.conda#049b7df8bae5e184d1de42cdf64855f8
+https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.4.0-hd590300_0.conda#b26e8aa824079e1be0294e7152ca4559
 https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.2.13-hd590300_5.conda#f36c115f1ee199da648e0597ec2047ad
 https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.4.20240210-h59595ed_0.conda#97da8860a0da5413c7c98a3b3838a645
-https://conda.anaconda.org/conda-forge/linux-64/ninja-1.11.1-h924138e_0.conda#73a4953a2d9c115bdc10ff30a52f675f
+https://conda.anaconda.org/conda-forge/linux-64/ninja-1.12.0-h00ab1b0_0.conda#b048701d52e7cbb5f59ddd4d3b17bbf5
 https://conda.anaconda.org/conda-forge/linux-64/openssl-3.2.1-hd590300_1.conda#9d731343cff6ee2e5a25c4a091bf8e2a
 https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h36c2ea0_1001.tar.bz2#22dad4df6e8630e8dff2428f6f6a7036
 https://conda.anaconda.org/conda-forge/linux-64/xorg-kbproto-1.0.7-h7f98852_1002.tar.bz2#4b230e8381279d76131116660f5a241a
@@ -32,9 +32,9 @@ https://conda.anaconda.org/conda-forge/linux-64/xz-5.2.6-h166bdaf_0.tar.bz2#2161
 https://conda.anaconda.org/conda-forge/linux-64/expat-2.6.2-h59595ed_0.conda#53fb86322bdb89496d7579fe3f02fd61
 https://conda.anaconda.org/conda-forge/linux-64/libbrotlidec-1.1.0-hd590300_1.conda#f07002e225d7a60a694d42a7bf5ff53f
 https://conda.anaconda.org/conda-forge/linux-64/libbrotlienc-1.1.0-hd590300_1.conda#5fc11c6020d421960607d821310fcd4d
-https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-13.2.0-h69a702a_5.conda#e73e9cfd1191783392131e6238bdb3e9
+https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-13.2.0-h69a702a_6.conda#3666a850342f8f3be88f9a93d948d027
 https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.43-h2797004_0.conda#009981dd9cfcaa4dbfa25ffaed86bcae
-https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.45.2-h2797004_0.conda#866983a220e27a80cb75e85cb30466a1
+https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.45.3-h2797004_0.conda#b3316cbe90249da4f8e84cd66e1cc55b
 https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.15-h0b41bf4_0.conda#33277193f5b92bad9fdd230eb700929c
 https://conda.anaconda.org/conda-forge/linux-64/readline-8.2-h8228510_1.conda#47d31b792659ce70f470b5c82fdfb7a4
 https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_h4845f30_101.conda#d453b98d9c83e71da0741bb0ff4d76bc
@@ -47,7 +47,7 @@ https://conda.anaconda.org/conda-forge/linux-64/libhiredis-1.0.2-h2cc385e_0.tar.
 https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.27-pthreads_h413a1c8_0.conda#a356024784da6dfd4683dc5ecf45b155
 https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.6.0-h1dd3fc0_3.conda#66f03896ffbe1a110ffda05c7a856504
 https://conda.anaconda.org/conda-forge/linux-64/llvm-openmp-18.1.3-h4dfa4b3_0.conda#d39965123dffcad4d750989be65bcb7c
-https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.45.2-h2c6b66d_0.conda#1423efca06ed343c1da0fc429bae0779
+https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.45.3-h2c6b66d_0.conda#be7d70f2db41b674733667bdd69bd000
 https://conda.anaconda.org/conda-forge/linux-64/xorg-libx11-1.8.9-h8ee46fc_0.conda#077b6e8ad6a3ddb741fce2496dd01bec
 https://conda.anaconda.org/conda-forge/linux-64/brotli-1.1.0-hd590300_1.conda#f27a24d46e3ea7b70a1f98e50c62508f
 https://conda.anaconda.org/conda-forge/linux-64/ccache-4.9.1-h1fcd64f_0.conda#3620f564bcf28c3524951b6f64f5c5ac
@@ -72,7 +72,7 @@ https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyh9f0ad1d_0.tar.bz2
 https://conda.anaconda.org/conda-forge/linux-64/numpy-1.26.4-py39h6dedee3_0.conda#557d64563e84ff21b14f586c7f662b7f
 https://conda.anaconda.org/conda-forge/noarch/packaging-24.0-pyhd8ed1ab_0.conda#248f521b64ce055e7feae3105e7abeb8
 https://conda.anaconda.org/conda-forge/linux-64/pillow-10.3.0-py39h90a76f3_0.conda#799e6519cfffe2784db27b1db2ef33f3
-https://conda.anaconda.org/conda-forge/noarch/pluggy-1.4.0-pyhd8ed1ab_0.conda#139e9feb65187e916162917bb2484976
+https://conda.anaconda.org/conda-forge/noarch/pluggy-1.5.0-pyhd8ed1ab_0.conda#d3483c8fc2dc2cc3f5cf43e26d60cabf
 https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.1.2-pyhd8ed1ab_0.conda#b9a4dacf97241704529131a0dfc0494f
 https://conda.anaconda.org/conda-forge/noarch/pypy-7.3.15-1_pypy39.conda#a418a6c16bd6f7ed56b92194214791a0
 https://conda.anaconda.org/conda-forge/noarch/setuptools-69.5.1-pyhd8ed1ab_0.conda#7462280d81f639363e6e63c81276bd9e
@@ -90,13 +90,13 @@ https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.4.0-pyhd8ed1
 https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.0-pyhd8ed1ab_0.conda#e0ed1bf13ce3a440e022157bf4764465
 https://conda.anaconda.org/conda-forge/noarch/meson-1.4.0-pyhd8ed1ab_0.conda#52a0660cfa40b45bf254ecc3374cb2e0
 https://conda.anaconda.org/conda-forge/noarch/pip-24.0-pyhd8ed1ab_0.conda#f586ac1e56c8638b64f9c8122a7b8a67
-https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.7.1-pyhd8ed1ab_0.conda#dcb27826ffc94d5f04e241322239983b
+https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.8.0-pyhd8ed1ab_0.conda#573fe09d7bd0cd4bcc210d8369b5ca47
 https://conda.anaconda.org/conda-forge/noarch/pytest-7.4.4-pyhd8ed1ab_0.conda#a9d145de8c5f064b5fa68fb34725d9f4
 https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0-pyhd8ed1ab_0.conda#2cf4264fffb9e6eff6031c5b6884d61c
 https://conda.anaconda.org/conda-forge/linux-64/scipy-1.12.0-py39h6dedee3_2.conda#6c5d74bac41838f4377dfd45085e1fec
 https://conda.anaconda.org/conda-forge/linux-64/blas-2.122-openblas.conda#5065468105542a8b23ea47bd8b6fa55f
 https://conda.anaconda.org/conda-forge/noarch/importlib-resources-6.4.0-pyhd8ed1ab_0.conda#dcbadab7a68738a028e195ab68ab2d2e
-https://conda.anaconda.org/conda-forge/noarch/meson-python-0.15.0-pyh0c530f3_0.conda#3bc64565ca78ce3bb80248d09926d8f9
+https://conda.anaconda.org/conda-forge/noarch/meson-python-0.16.0-pyh0c530f3_0.conda#e16f0dbf502da873be9f9adb0dc52547
 https://conda.anaconda.org/conda-forge/linux-64/pyamg-5.1.0-py39h5fd064f_0.conda#04676d2a49da3cb608af77e04b796ce1
 https://conda.anaconda.org/conda-forge/noarch/pytest-xdist-3.5.0-pyhd8ed1ab_0.conda#d5f595da2daead898ca958ac62f0307b
 https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.8.4-py39h4e7d633_0.conda#58272019e595dde98d0844ae3ebf0cfe
diff --git a/build_tools/azure/ubuntu_atlas_lock.txt b/build_tools/azure/ubuntu_atlas_lock.txt
index c8e94493ccf28..f2110d2173a3c 100644
--- a/build_tools/azure/ubuntu_atlas_lock.txt
+++ b/build_tools/azure/ubuntu_atlas_lock.txt
@@ -35,7 +35,7 @@ pytest==7.4.4
     #   pytest-xdist
 pytest-xdist==3.5.0
     # via -r build_tools/azure/ubuntu_atlas_requirements.txt
-threadpoolctl==2.0.0
+threadpoolctl==3.1.0
     # via -r build_tools/azure/ubuntu_atlas_requirements.txt
 tomli==2.0.1
     # via
diff --git a/build_tools/azure/ubuntu_atlas_requirements.txt b/build_tools/azure/ubuntu_atlas_requirements.txt
index aab362dda0bf2..805d84d4d0aac 100644
--- a/build_tools/azure/ubuntu_atlas_requirements.txt
+++ b/build_tools/azure/ubuntu_atlas_requirements.txt
@@ -3,7 +3,7 @@
 # build_tools/update_environments_and_lock_files.py
 cython==3.0.10  # min
 joblib==1.2.0  # min
-threadpoolctl==2.0.0  # min
+threadpoolctl==3.1.0  # min
 pytest<8
 pytest-xdist
 ninja
diff --git a/build_tools/circle/doc_linux-64_conda.lock b/build_tools/circle/doc_linux-64_conda.lock
index 3f05468863ae6..60bbe8298c34c 100644
--- a/build_tools/circle/doc_linux-64_conda.lock
+++ b/build_tools/circle/doc_linux-64_conda.lock
@@ -318,5 +318,5 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1
 # pip nbclient @ https://files.pythonhosted.org/packages/66/e8/00517a23d3eeaed0513e718fbc94aab26eaa1758f5690fc8578839791c79/nbclient-0.10.0-py3-none-any.whl#sha256=f13e3529332a1f1f81d82a53210322476a168bb7090a0289c795fe9cc11c9d3f
 # pip nbconvert @ https://files.pythonhosted.org/packages/23/8a/8d67cbd984739247e4b205c1143e2f71b25b4f71e180fe70f7cb2cf02633/nbconvert-7.16.3-py3-none-any.whl#sha256=ddeff14beeeedf3dd0bc506623e41e4507e551736de59df69a91f86700292b3b
 # pip jupyter-server @ https://files.pythonhosted.org/packages/07/46/6bb926b3bf878bf687b952fb6a4c09d014b4575a25960f2cd1a61793763f/jupyter_server-2.14.0-py3-none-any.whl#sha256=fb6be52c713e80e004fac34b35a0990d6d36ba06fd0a2b2ed82b899143a64210
-# pip jupyterlab-server @ https://files.pythonhosted.org/packages/6a/c9/b270a875916b18f137bb30ecd05031a2f05c95d47a8e8fbd3f805a72f593/jupyterlab_server-2.26.0-py3-none-any.whl#sha256=54622cbd330526a385ee0c1fdccdff3a1e7219bf3e864a335284a1270a1973df
+# pip jupyterlab-server @ https://files.pythonhosted.org/packages/2f/b9/ed4ecad7cf1863a64920dc4c19b0376628b5d6bd28d2ec1e00cbac4ba2fb/jupyterlab_server-2.27.1-py3-none-any.whl#sha256=f5e26156e5258b24d532c84e7c74cc212e203bff93eb856f81c24c16daeecc75
 # pip jupyterlite-sphinx @ https://files.pythonhosted.org/packages/38/c9/5f1142c005cf8d75830b10029e53f074324bc85cfca1f1d0f22a207b771c/jupyterlite_sphinx-0.9.3-py3-none-any.whl#sha256=be6332d16490ea2fa90b78187a2c5e1c357195966a25741d60b1790346571041
diff --git a/build_tools/update_environments_and_lock_files.py b/build_tools/update_environments_and_lock_files.py
index fd77cfd3c0721..8a4a30d235e0b 100644
--- a/build_tools/update_environments_and_lock_files.py
+++ b/build_tools/update_environments_and_lock_files.py
@@ -141,7 +141,10 @@ def remove_from(alist, to_remove):
         "folder": "build_tools/azure",
         "platform": "osx-64",
         "channel": "defaults",
-        "conda_dependencies": remove_from(common_dependencies, ["cython"]) + ["ccache"],
+        "conda_dependencies": remove_from(
+            common_dependencies, ["cython", "threadpoolctl"]
+        )
+        + ["ccache"],
         "package_constraints": {
             "blas": "[build=mkl]",
             # scipy 1.12.x crashes on this platform (https://github.com/scipy/scipy/pull/20086)
@@ -149,9 +152,9 @@ def remove_from(alist, to_remove):
             # channel.
             "scipy": "<1.12",
         },
-        # TODO: put cython back to conda dependencies when required version is
-        # available on the main channel
-        "pip_dependencies": ["cython"],
+        # TODO: put cython and threadpoolctl back to conda dependencies when required
+        # version is available on the main channel
+        "pip_dependencies": ["cython", "threadpoolctl"],
     },
     {
         "name": "pymin_conda_defaults_openblas",
@@ -161,7 +164,8 @@ def remove_from(alist, to_remove):
         "platform": "linux-64",
         "channel": "defaults",
         "conda_dependencies": remove_from(
-            common_dependencies, ["pandas", "cython", "pip", "ninja", "meson-python"]
+            common_dependencies,
+            ["pandas", "cython", "threadpoolctl", "pip", "ninja", "meson-python"],
         )
         + ["ccache"],
         "package_constraints": {
@@ -170,12 +174,11 @@ def remove_from(alist, to_remove):
             "numpy": "1.21",  # the min version is not available on the defaults channel
             "scipy": "1.7",  # the min version has some low level crashes
             "matplotlib": "min",
-            "threadpoolctl": "2.2.0",
             "cython": "min",
         },
-        # TODO: put cython back to conda dependencies when required version is
-        # available on the main channel
-        "pip_dependencies": ["cython"],
+        # TODO: put cython and threadpoolctl back to conda dependencies when required
+        # version is available on the main channel
+        "pip_dependencies": ["cython", "threadpoolctl"],
     },
     {
         "name": "pymin_conda_forge_openblas_ubuntu_2204",
@@ -381,7 +384,7 @@ def remove_from(alist, to_remove):
         ],
         "package_constraints": {
             "joblib": "min",
-            "threadpoolctl": "2.2.0",
+            "threadpoolctl": "3.1.0",
             "pytest": "min",
             "pytest-cov": "min",
             # no pytest-xdist because it causes issue on 32bit
diff --git a/pyproject.toml b/pyproject.toml
index 828569ecc71ee..69d9702716cb5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -10,7 +10,7 @@ dependencies = [
   "numpy>=1.19.5",
   "scipy>=1.6.0",
   "joblib>=1.2.0",
-  "threadpoolctl>=2.0.0",
+  "threadpoolctl>=3.1.0",
 ]
 requires-python = ">=3.9"
 license = {text = "new BSD"}
@@ -45,7 +45,7 @@ tracker = "https://github.com/scikit-learn/scikit-learn/issues"
 
 [project.optional-dependencies]
 build = ["numpy>=1.19.5", "scipy>=1.6.0", "cython>=3.0.10", "meson-python>=0.15.0"]
-install = ["numpy>=1.19.5", "scipy>=1.6.0", "joblib>=1.2.0", "threadpoolctl>=2.0.0"]
+install = ["numpy>=1.19.5", "scipy>=1.6.0", "joblib>=1.2.0", "threadpoolctl>=3.1.0"]
 benchmark = ["matplotlib>=3.3.4", "pandas>=1.1.5", "memory_profiler>=0.57.0"]
 docs = [
     "matplotlib>=3.3.4",
diff --git a/sklearn/__init__.py b/sklearn/__init__.py
index 30a3bbcdcbf66..45a26334a25f5 100644
--- a/sklearn/__init__.py
+++ b/sklearn/__init__.py
@@ -73,6 +73,15 @@
     # We are not importing the rest of scikit-learn during the build
     # process, as it may not be compiled yet
 else:
+    # Import numpy, scipy to make sure that the BLAS libs are loaded before
+    # creating the ThreadpoolController. They would be imported just after
+    # when importing utils anyway. This makes it explicit and robust to changes
+    # in utils.
+    # (OpenMP is loaded by importing show_versions right after this block)
+    import numpy  # noqa
+    import scipy.linalg  # noqa
+    from threadpoolctl import ThreadpoolController
+
     # `_distributor_init` allows distributors to run custom init code.
     # For instance, for the Windows wheel, this is used to pre-load the
     # vcomp shared library runtime for OpenMP embedded in the sklearn/.libs
@@ -141,6 +150,12 @@
     except ModuleNotFoundError:
         pass
 
+    # Set a global controller that can be used to locally limit the number of
+    # threads without looping through all shared libraries every time.
+    # This instantitation should not happen earlier because it needs all BLAS and
+    # OpenMP libs to be loaded first.
+    _threadpool_controller = ThreadpoolController()
+
 
 def setup_module(module):
     """Fixture for the tests to assure globally controllable seeding of RNGs"""
diff --git a/sklearn/_min_dependencies.py b/sklearn/_min_dependencies.py
index a487a048c53c1..00315f31d4c3f 100644
--- a/sklearn/_min_dependencies.py
+++ b/sklearn/_min_dependencies.py
@@ -7,7 +7,7 @@
 NUMPY_MIN_VERSION = "1.19.5"
 SCIPY_MIN_VERSION = "1.6.0"
 JOBLIB_MIN_VERSION = "1.2.0"
-THREADPOOLCTL_MIN_VERSION = "2.0.0"
+THREADPOOLCTL_MIN_VERSION = "3.1.0"
 PYTEST_MIN_VERSION = "7.1.2"
 CYTHON_MIN_VERSION = "3.0.10"
 
diff --git a/sklearn/cluster/_kmeans.py b/sklearn/cluster/_kmeans.py
index 16df12838d91f..2ab6f1e95563b 100644
--- a/sklearn/cluster/_kmeans.py
+++ b/sklearn/cluster/_kmeans.py
@@ -18,6 +18,7 @@
 import numpy as np
 import scipy.sparse as sp
 
+from .. import _threadpool_controller
 from ..base import (
     BaseEstimator,
     ClassNamePrefixFeaturesOutMixin,
@@ -31,7 +32,6 @@
 from ..utils._openmp_helpers import _openmp_effective_n_threads
 from ..utils._param_validation import Interval, StrOptions, validate_params
 from ..utils.extmath import row_norms, stable_cumsum
-from ..utils.fixes import threadpool_info, threadpool_limits
 from ..utils.sparsefuncs import mean_variance_axis
 from ..utils.sparsefuncs_fast import assign_rows_csr
 from ..utils.validation import (
@@ -622,6 +622,9 @@ def _kmeans_single_elkan(
     return labels, inertia, centers, i + 1
 
 
+# Threadpoolctl context to limit the number of threads in second level of
+# nested parallelism (i.e. BLAS) to avoid oversubscription.
+@_threadpool_controller.wrap(limits=1, user_api="blas")
 def _kmeans_single_lloyd(
     X,
     sample_weight,
@@ -697,59 +700,56 @@ def _kmeans_single_lloyd(
 
     strict_convergence = False
 
-    # Threadpoolctl context to limit the number of threads in second level of
-    # nested parallelism (i.e. BLAS) to avoid oversubscription.
-    with threadpool_limits(limits=1, user_api="blas"):
-        for i in range(max_iter):
-            lloyd_iter(
-                X,
-                sample_weight,
-                centers,
-                centers_new,
-                weight_in_clusters,
-                labels,
-                center_shift,
-                n_threads,
-            )
+    for i in range(max_iter):
+        lloyd_iter(
+            X,
+            sample_weight,
+            centers,
+            centers_new,
+            weight_in_clusters,
+            labels,
+            center_shift,
+            n_threads,
+        )
 
-            if verbose:
-                inertia = _inertia(X, sample_weight, centers, labels, n_threads)
-                print(f"Iteration {i}, inertia {inertia}.")
+        if verbose:
+            inertia = _inertia(X, sample_weight, centers, labels, n_threads)
+            print(f"Iteration {i}, inertia {inertia}.")
 
-            centers, centers_new = centers_new, centers
+        centers, centers_new = centers_new, centers
 
-            if np.array_equal(labels, labels_old):
-                # First check the labels for strict convergence.
+        if np.array_equal(labels, labels_old):
+            # First check the labels for strict convergence.
+            if verbose:
+                print(f"Converged at iteration {i}: strict convergence.")
+            strict_convergence = True
+            break
+        else:
+            # No strict convergence, check for tol based convergence.
+            center_shift_tot = (center_shift**2).sum()
+            if center_shift_tot <= tol:
                 if verbose:
-                    print(f"Converged at iteration {i}: strict convergence.")
-                strict_convergence = True
+                    print(
+                        f"Converged at iteration {i}: center shift "
+                        f"{center_shift_tot} within tolerance {tol}."
+                    )
                 break
-            else:
-                # No strict convergence, check for tol based convergence.
-                center_shift_tot = (center_shift**2).sum()
-                if center_shift_tot <= tol:
-                    if verbose:
-                        print(
-                            f"Converged at iteration {i}: center shift "
-                            f"{center_shift_tot} within tolerance {tol}."
-                        )
-                    break
 
-            labels_old[:] = labels
+        labels_old[:] = labels
 
-        if not strict_convergence:
-            # rerun E-step so that predicted labels match cluster centers
-            lloyd_iter(
-                X,
-                sample_weight,
-                centers,
-                centers,
-                weight_in_clusters,
-                labels,
-                center_shift,
-                n_threads,
-                update_centers=False,
-            )
+    if not strict_convergence:
+        # rerun E-step so that predicted labels match cluster centers
+        lloyd_iter(
+            X,
+            sample_weight,
+            centers,
+            centers,
+            weight_in_clusters,
+            labels,
+            center_shift,
+            n_threads,
+            update_centers=False,
+        )
 
     inertia = _inertia(X, sample_weight, centers, labels, n_threads)
 
@@ -826,14 +826,10 @@ def _labels_inertia(X, sample_weight, centers, n_threads=1, return_inertia=True)
     return labels
 
 
-def _labels_inertia_threadpool_limit(
-    X, sample_weight, centers, n_threads=1, return_inertia=True
-):
-    """Same as _labels_inertia but in a threadpool_limits context."""
-    with threadpool_limits(limits=1, user_api="blas"):
-        result = _labels_inertia(X, sample_weight, centers, n_threads, return_inertia)
-
-    return result
+# Same as _labels_inertia but in a threadpool_limits context.
+_labels_inertia_threadpool_limit = _threadpool_controller.wrap(
+    limits=1, user_api="blas"
+)(_labels_inertia)
 
 
 class _BaseKMeans(
@@ -926,7 +922,7 @@ def _check_mkl_vcomp(self, X, n_samples):
 
         n_active_threads = int(np.ceil(n_samples / CHUNK_SIZE))
         if n_active_threads < self._n_threads:
-            modules = threadpool_info()
+            modules = _threadpool_controller.info()
             has_vcomp = "vcomp" in [module["prefix"] for module in modules]
             has_mkl = ("mkl", "intel") in [
                 (module["internal_api"], module.get("threading_layer", None))
@@ -2148,7 +2144,7 @@ def fit(self, X, y=None, sample_weight=None):
 
         n_steps = (self.max_iter * n_samples) // self._batch_size
 
-        with threadpool_limits(limits=1, user_api="blas"):
+        with _threadpool_controller.limit(limits=1, user_api="blas"):
             # Perform the iterative optimization until convergence
             for i in range(n_steps):
                 # Sample a minibatch from the full dataset
@@ -2274,7 +2270,7 @@ def partial_fit(self, X, y=None, sample_weight=None):
             # Initialize number of samples seen since last reassignment
             self._n_since_last_reassign = 0
 
-        with threadpool_limits(limits=1, user_api="blas"):
+        with _threadpool_controller.limit(limits=1, user_api="blas"):
             _mini_batch_step(
                 X,
                 sample_weight=sample_weight,
diff --git a/sklearn/cluster/tests/test_k_means.py b/sklearn/cluster/tests/test_k_means.py
index b6f718dce03e0..c3a41a65de632 100644
--- a/sklearn/cluster/tests/test_k_means.py
+++ b/sklearn/cluster/tests/test_k_means.py
@@ -8,6 +8,7 @@
 import pytest
 from scipy import sparse as sp
 
+from sklearn import _threadpool_controller
 from sklearn.base import clone
 from sklearn.cluster import KMeans, MiniBatchKMeans, k_means, kmeans_plusplus
 from sklearn.cluster._k_means_common import (
@@ -31,7 +32,7 @@
     create_memmap_backed_data,
 )
 from sklearn.utils.extmath import row_norms
-from sklearn.utils.fixes import CSR_CONTAINERS, threadpool_limits
+from sklearn.utils.fixes import CSR_CONTAINERS
 
 # non centered, sparse centers to check the
 centers = np.array(
@@ -967,13 +968,13 @@ def test_result_equal_in_diff_n_threads(Estimator, global_random_seed):
     rnd = np.random.RandomState(global_random_seed)
     X = rnd.normal(size=(50, 10))
 
-    with threadpool_limits(limits=1, user_api="openmp"):
+    with _threadpool_controller.limit(limits=1, user_api="openmp"):
         result_1 = (
             Estimator(n_clusters=n_clusters, random_state=global_random_seed)
             .fit(X)
             .labels_
         )
-    with threadpool_limits(limits=2, user_api="openmp"):
+    with _threadpool_controller.limit(limits=2, user_api="openmp"):
         result_2 = (
             Estimator(n_clusters=n_clusters, random_state=global_random_seed)
             .fit(X)
diff --git a/sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx.tp b/sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx.tp
index a686153c3ac9c..ef61158fedca8 100644
--- a/sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx.tp
+++ b/sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx.tp
@@ -14,7 +14,7 @@ from numbers import Integral
 from scipy.sparse import issparse
 from ...utils import check_array, check_scalar
 from ...utils.fixes import _in_unstable_openblas_configuration
-from ...utils.fixes import threadpool_limits
+from ... import _threadpool_controller
 
 {{for name_suffix in ['64', '32']}}
 
@@ -58,7 +58,7 @@ cdef class ArgKmin{{name_suffix}}(BaseDistancesReduction{{name_suffix}}):
         """
         # Limit the number of threads in second level of nested parallelism for BLAS
         # to avoid threads over-subscription (in DOT or GEMM for instance).
-        with threadpool_limits(limits=1, user_api='blas'):
+        with _threadpool_controller.limit(limits=1, user_api='blas'):
           if metric in ("euclidean", "sqeuclidean"):
               # Specialized implementation of ArgKmin for the Euclidean distance
               # for the dense-dense and sparse-sparse cases.
diff --git a/sklearn/metrics/_pairwise_distances_reduction/_argkmin_classmode.pyx.tp b/sklearn/metrics/_pairwise_distances_reduction/_argkmin_classmode.pyx.tp
index f9719f6959dfc..b875499f44ed4 100644
--- a/sklearn/metrics/_pairwise_distances_reduction/_argkmin_classmode.pyx.tp
+++ b/sklearn/metrics/_pairwise_distances_reduction/_argkmin_classmode.pyx.tp
@@ -4,10 +4,10 @@ from libcpp.map cimport map as cpp_map, pair as cpp_pair
 from libc.stdlib cimport free
 
 from ...utils._typedefs cimport intp_t, float64_t
+from ... import _threadpool_controller
 
 import numpy as np
 from scipy.sparse import issparse
-from sklearn.utils.fixes import threadpool_limits
 from ._classmode cimport WeightingStrategy
 
 {{for name_suffix in ["32", "64"]}}
@@ -66,7 +66,7 @@ cdef class ArgKminClassMode{{name_suffix}}(ArgKmin{{name_suffix}}):
 
         # Limit the number of threads in second level of nested parallelism for BLAS
         # to avoid threads over-subscription (in GEMM for instance).
-        with threadpool_limits(limits=1, user_api="blas"):
+        with _threadpool_controller.limit(limits=1, user_api="blas"):
             if pda.execute_in_parallel_on_Y:
                 pda._parallel_on_Y()
             else:
diff --git a/sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors.pyx.tp b/sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors.pyx.tp
index dcc97b4d32fd4..f4af378062bdc 100644
--- a/sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors.pyx.tp
+++ b/sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors.pyx.tp
@@ -17,7 +17,7 @@ from numbers import Real
 from scipy.sparse import issparse
 from ...utils import check_array, check_scalar
 from ...utils.fixes import _in_unstable_openblas_configuration
-from ...utils.fixes import threadpool_limits
+from ... import _threadpool_controller
 
 cnp.import_array()
 
@@ -110,7 +110,7 @@ cdef class RadiusNeighbors{{name_suffix}}(BaseDistancesReduction{{name_suffix}})
 
         # Limit the number of threads in second level of nested parallelism for BLAS
         # to avoid threads over-subscription (in GEMM for instance).
-        with threadpool_limits(limits=1, user_api="blas"):
+        with _threadpool_controller.limit(limits=1, user_api="blas"):
             if pda.execute_in_parallel_on_Y:
                 pda._parallel_on_Y()
             else:
diff --git a/sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors_classmode.pyx.tp b/sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors_classmode.pyx.tp
index 25067b43cd20c..ab12d7904c7fd 100644
--- a/sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors_classmode.pyx.tp
+++ b/sklearn/metrics/_pairwise_distances_reduction/_radius_neighbors_classmode.pyx.tp
@@ -8,7 +8,7 @@ from ...utils._typedefs cimport intp_t, float64_t
 
 import numpy as np
 from scipy.sparse import issparse
-from ...utils.fixes import threadpool_limits
+from ... import _threadpool_controller
 
 
 {{for name_suffix in ["32", "64"]}}
@@ -60,7 +60,7 @@ cdef class RadiusNeighborsClassMode{{name_suffix}}(RadiusNeighbors{{name_suffix}
 
         # Limit the number of threads in second level of nested parallelism for BLAS
         # to avoid threads over-subscription (in GEMM for instance).
-        with threadpool_limits(limits=1, user_api="blas"):
+        with _threadpool_controller.limit(limits=1, user_api="blas"):
             if pda.execute_in_parallel_on_Y:
                 pda._parallel_on_Y()
             else:
diff --git a/sklearn/metrics/tests/test_pairwise_distances_reduction.py b/sklearn/metrics/tests/test_pairwise_distances_reduction.py
index e5983f9273d94..95dfa98178ee7 100644
--- a/sklearn/metrics/tests/test_pairwise_distances_reduction.py
+++ b/sklearn/metrics/tests/test_pairwise_distances_reduction.py
@@ -5,9 +5,9 @@
 
 import numpy as np
 import pytest
-import threadpoolctl
 from scipy.spatial.distance import cdist
 
+from sklearn import _threadpool_controller
 from sklearn.metrics import euclidean_distances, pairwise_distances
 from sklearn.metrics._pairwise_distances_reduction import (
     ArgKmin,
@@ -1200,7 +1200,7 @@ def test_n_threads_agnosticism(
         **compute_parameters,
     )
 
-    with threadpoolctl.threadpool_limits(limits=1, user_api="openmp"):
+    with _threadpool_controller.limit(limits=1, user_api="openmp"):
         dist, indices = Dispatcher.compute(
             X,
             Y,
diff --git a/sklearn/utils/_show_versions.py b/sklearn/utils/_show_versions.py
index 1431108477263..cc17b71b23799 100644
--- a/sklearn/utils/_show_versions.py
+++ b/sklearn/utils/_show_versions.py
@@ -9,8 +9,9 @@
 import platform
 import sys
 
+from threadpoolctl import threadpool_info
+
 from .. import __version__
-from ..utils.fixes import threadpool_info
 from ._openmp_helpers import _openmp_parallelism_enabled
 
 
diff --git a/sklearn/utils/fixes.py b/sklearn/utils/fixes.py
index e33519a3154d8..21e62150b0356 100644
--- a/sklearn/utils/fixes.py
+++ b/sklearn/utils/fixes.py
@@ -18,7 +18,6 @@
 import scipy
 import scipy.sparse.linalg
 import scipy.stats
-import threadpoolctl
 
 import sklearn
 
@@ -97,42 +96,6 @@ def _percentile(a, q, *, method="linear", **kwargs):
     from numpy import percentile  # type: ignore  # noqa
 
 
-# compatibility fix for threadpoolctl >= 3.0.0
-# since version 3 it's possible to setup a global threadpool controller to avoid
-# looping through all loaded shared libraries each time.
-# the global controller is created during the first call to threadpoolctl.
-def _get_threadpool_controller():
-    if not hasattr(threadpoolctl, "ThreadpoolController"):
-        return None
-
-    if not hasattr(sklearn, "_sklearn_threadpool_controller"):
-        sklearn._sklearn_threadpool_controller = threadpoolctl.ThreadpoolController()
-
-    return sklearn._sklearn_threadpool_controller
-
-
-def threadpool_limits(limits=None, user_api=None):
-    controller = _get_threadpool_controller()
-    if controller is not None:
-        return controller.limit(limits=limits, user_api=user_api)
-    else:
-        return threadpoolctl.threadpool_limits(limits=limits, user_api=user_api)
-
-
-threadpool_limits.__doc__ = threadpoolctl.threadpool_limits.__doc__
-
-
-def threadpool_info():
-    controller = _get_threadpool_controller()
-    if controller is not None:
-        return controller.info()
-    else:
-        return threadpoolctl.threadpool_info()
-
-
-threadpool_info.__doc__ = threadpoolctl.threadpool_info.__doc__
-
-
 # TODO: Remove when SciPy 1.11 is the minimum supported version
 def _mode(a, axis=0):
     if sp_version >= parse_version("1.9.0"):
@@ -428,7 +391,7 @@ def _in_unstable_openblas_configuration():
     import numpy  # noqa
     import scipy  # noqa
 
-    modules_info = threadpool_info()
+    modules_info = sklearn._threadpool_controller.info()
 
     open_blas_used = any(info["internal_api"] == "openblas" for info in modules_info)
     if not open_blas_used:
diff --git a/sklearn/utils/tests/test_show_versions.py b/sklearn/utils/tests/test_show_versions.py
index bd166dfd8e522..aade231e46f56 100644
--- a/sklearn/utils/tests/test_show_versions.py
+++ b/sklearn/utils/tests/test_show_versions.py
@@ -1,6 +1,7 @@
+from threadpoolctl import threadpool_info
+
 from sklearn.utils._show_versions import _get_deps_info, _get_sys_info, show_versions
 from sklearn.utils._testing import ignore_warnings
-from sklearn.utils.fixes import threadpool_info
 
 
 def test_get_sys_info():