diff --git a/sklearn/impute/_base.py b/sklearn/impute/_base.py
index 1842a0527891a..225ae249b2107 100644
--- a/sklearn/impute/_base.py
+++ b/sklearn/impute/_base.py
@@ -9,10 +9,10 @@
 import numpy as np
 import numpy.ma as ma
 from scipy import sparse as sp
-from scipy import stats
 
 from ..base import BaseEstimator, TransformerMixin
 from ..utils._param_validation import StrOptions
+from ..utils.fixes import _mode
 from ..utils.sparsefuncs import _get_median
 from ..utils.validation import check_is_fitted
 from ..utils.validation import FLOAT_DTYPES
@@ -52,7 +52,7 @@ def _most_frequent(array, extra_value, n_repeat):
                 if count == most_frequent_count
             )
         else:
-            mode = stats.mode(array)
+            mode = _mode(array)
             most_frequent_value = mode[0][0]
             most_frequent_count = mode[1][0]
     else:
diff --git a/sklearn/neighbors/_classification.py b/sklearn/neighbors/_classification.py
index 25ee67728e1e7..eebd615b2491c 100644
--- a/sklearn/neighbors/_classification.py
+++ b/sklearn/neighbors/_classification.py
@@ -10,7 +10,7 @@
 from numbers import Integral
 
 import numpy as np
-from scipy import stats
+from ..utils.fixes import _mode
 from ..utils.extmath import weighted_mode
 from ..utils.validation import _is_arraylike, _num_samples
 
@@ -249,7 +249,7 @@ def predict(self, X):
         y_pred = np.empty((n_queries, n_outputs), dtype=classes_[0].dtype)
         for k, classes_k in enumerate(classes_):
             if weights is None:
-                mode, _ = stats.mode(_y[neigh_ind, k], axis=1)
+                mode, _ = _mode(_y[neigh_ind, k], axis=1)
             else:
                 mode, _ = weighted_mode(_y[neigh_ind, k], weights, axis=1)
 
diff --git a/sklearn/utils/fixes.py b/sklearn/utils/fixes.py
index b0074ae7e3a18..cdd63e00cd381 100644
--- a/sklearn/utils/fixes.py
+++ b/sklearn/utils/fixes.py
@@ -163,3 +163,10 @@ def threadpool_info():
 
 
 threadpool_info.__doc__ = threadpoolctl.threadpool_info.__doc__
+
+
+# TODO: Remove when SciPy 1.9 is the minimum supported version
+def _mode(a, axis=0):
+    if sp_version >= parse_version("1.9.0"):
+        return scipy.stats.mode(a, axis=axis, keepdims=True)
+    return scipy.stats.mode(a, axis=axis)
diff --git a/sklearn/utils/tests/test_extmath.py b/sklearn/utils/tests/test_extmath.py
index 07a553c8cf09d..14e541bbef2dc 100644
--- a/sklearn/utils/tests/test_extmath.py
+++ b/sklearn/utils/tests/test_extmath.py
@@ -6,7 +6,6 @@
 import numpy as np
 from scipy import sparse
 from scipy import linalg
-from scipy import stats
 from scipy.sparse.linalg import eigsh
 from scipy.special import expit
 
@@ -19,6 +18,7 @@
 from sklearn.utils._testing import assert_array_equal
 from sklearn.utils._testing import assert_array_almost_equal
 from sklearn.utils._testing import skip_if_32bit
+from sklearn.utils.fixes import _mode
 
 from sklearn.utils.extmath import density, _safe_accumulator_op
 from sklearn.utils.extmath import randomized_svd, _randomized_eigsh
@@ -56,7 +56,7 @@ def test_uniform_weights():
     weights = np.ones(x.shape)
 
     for axis in (None, 0, 1):
-        mode, score = stats.mode(x, axis)
+        mode, score = _mode(x, axis)
         mode2, score2 = weighted_mode(x, weights, axis=axis)
 
         assert_array_equal(mode, mode2)