diff --git a/doc/whats_new/upcoming_changes/sklearn.utils/26335.enhancement.rst b/doc/whats_new/upcoming_changes/sklearn.utils/26335.enhancement.rst
new file mode 100644
index 0000000000000..e5bf047cd5db9
--- /dev/null
+++ b/doc/whats_new/upcoming_changes/sklearn.utils/26335.enhancement.rst
@@ -0,0 +1,4 @@
+- |Enhancement| :func:`utils.multiclass.type_of_target` raises a warning when the number
+  of unique classes is greater than 50% of the number of samples. This warning is raised
+  only if `y` has more than 20 samples.
+  By :user:`Rahil Parikh <rprkh>`.
diff --git a/sklearn/utils/multiclass.py b/sklearn/utils/multiclass.py
index 15d1428ce2ad7..3a81e2b9eb6fe 100644
--- a/sklearn/utils/multiclass.py
+++ b/sklearn/utils/multiclass.py
@@ -413,7 +413,16 @@ def _raise_or_return():
     # Check multiclass
     if issparse(first_row_or_val):
         first_row_or_val = first_row_or_val.data
-    if cached_unique(y).shape[0] > 2 or (y.ndim == 2 and len(first_row_or_val) > 1):
+    classes = cached_unique(y)
+    if y.shape[0] > 20 and classes.shape[0] > round(0.5 * y.shape[0]):
+        # Only raise the warning when we have at least 20 samples.
+        warnings.warn(
+            "The number of unique classes is greater than 50% of the number "
+            "of samples.",
+            UserWarning,
+            stacklevel=2,
+        )
+    if classes.shape[0] > 2 or (y.ndim == 2 and len(first_row_or_val) > 1):
         # [1, 2, 3] or [[1., 2., 3]] or [[1, 2]]
         return "multiclass" + suffix
     else:
diff --git a/sklearn/utils/tests/test_multiclass.py b/sklearn/utils/tests/test_multiclass.py
index b400d675e5687..433e8118923fb 100644
--- a/sklearn/utils/tests/test_multiclass.py
+++ b/sklearn/utils/tests/test_multiclass.py
@@ -1,3 +1,4 @@
+import warnings
 from itertools import product
 
 import numpy as np
@@ -294,6 +295,25 @@ def test_unique_labels():
     assert_array_equal(unique_labels(np.ones((4, 5)), np.ones((5, 5))), np.arange(5))
 
 
+def test_type_of_target_too_many_unique_classes():
+    """Check that we raise a warning when the number of unique classes is greater than
+    50% of the number of samples.
+
+    We need to check that we don't raise if we have less than 20 samples.
+    """
+
+    y = np.arange(25)
+    msg = r"The number of unique classes is greater than 50% of the number of samples."
+    with pytest.warns(UserWarning, match=msg):
+        type_of_target(y)
+
+    # less than 20 samples, no warning should be raised
+    y = np.arange(10)
+    with warnings.catch_warnings():
+        warnings.simplefilter("error")
+        type_of_target(y)
+
+
 def test_unique_labels_non_specific():
     # Test unique_labels with a variety of collected examples