diff --git a/python/cuml/tests/test_linear_model.py b/python/cuml/tests/test_linear_model.py
index 77c06cd9fc..551ac764f3 100644
--- a/python/cuml/tests/test_linear_model.py
+++ b/python/cuml/tests/test_linear_model.py
@@ -359,6 +359,11 @@ def test_logistic_regression_unscaled(dtype, l1_ratio, C):
     assert score_test >= 0.94
 
 
+# Ignore scipy 1.17.0+ deprecation warning from sklearn 1.5.x LogisticRegression
+# using deprecated L-BFGS-B parameters. This is fixed in sklearn 1.6.0+.
+@pytest.mark.filterwarnings(
+    "ignore:.*The `disp` and `iprint` options.*:DeprecationWarning"
+)
 @given(dtype=dataset_dtypes())
 @example(dtype=np.float32)
 @example(dtype=np.float64)
@@ -570,6 +575,11 @@ def test_logistic_regression_input_type_consistency(constructor, dtype):
     assert isinstance(clf.predict(X), expected_type)
 
 
+# Ignore scipy 1.17.0+ deprecation warning from sklearn 1.5.x LogisticRegression
+# using deprecated L-BFGS-B parameters. This is fixed in sklearn 1.6.0+.
+@pytest.mark.filterwarnings(
+    "ignore:.*The `disp` and `iprint` options.*:DeprecationWarning"
+)
 @pytest.mark.parametrize(
     "y_kind", ["object", "fixed-string", "int32", "float32", "float16"]
 )
@@ -759,6 +769,11 @@ def test_logistic_predict_convert_dtype(dataset, test_dtype):
     use_sample_weight=False,
     class_weight_option=None,
 )
+# Ignore scipy 1.17.0+ deprecation warning from sklearn 1.5.x LogisticRegression
+# using deprecated L-BFGS-B parameters. This is fixed in sklearn 1.6.0+.
+@pytest.mark.filterwarnings(
+    "ignore:.*The `disp` and `iprint` options.*:DeprecationWarning"
+)
 def test_logistic_regression_weighting(
     dataset, use_sample_weight, class_weight_option
 ):
diff --git a/python/cuml/tests/test_sklearn_import_export.py b/python/cuml/tests/test_sklearn_import_export.py
index df22e72310..36428e17ec 100644
--- a/python/cuml/tests/test_sklearn_import_export.py
+++ b/python/cuml/tests/test_sklearn_import_export.py
@@ -213,6 +213,11 @@ def test_linear_regression(random_state):
     assert_estimator_roundtrip(original, SkLinearRegression, X, y)
 
 
+# Ignore scipy 1.17.0+ deprecation warning from sklearn 1.5.x LogisticRegression
+# using deprecated L-BFGS-B parameters. This is fixed in sklearn 1.6.0+.
+@pytest.mark.filterwarnings(
+    "ignore:.*The `disp` and `iprint` options.*:DeprecationWarning"
+)
 def test_logistic_regression(random_state):
     X, y = make_classification(
         n_samples=50, n_features=5, n_informative=3, random_state=random_state