diff --git a/python-package/xgboost/sklearn.py b/python-package/xgboost/sklearn.py
index cb738477b512..d5e20439ad41 100644
--- a/python-package/xgboost/sklearn.py
+++ b/python-package/xgboost/sklearn.py
@@ -248,7 +248,7 @@ def task(i: int) -> float:
         Balancing of positive and negative weights.
     base_score : Optional[float]
         The initial prediction score of all instances, global bias.
-    random_state : Optional[Union[numpy.random.RandomState, int]]
+    random_state : Optional[Union[numpy.random.RandomState, numpy.random.Generator, int]]
         Random number seed.
 
         .. note::
@@ -651,7 +651,9 @@ def __init__(
         reg_lambda: Optional[float] = None,
         scale_pos_weight: Optional[float] = None,
         base_score: Optional[float] = None,
-        random_state: Optional[Union[np.random.RandomState, int]] = None,
+        random_state: Optional[
+            Union[np.random.RandomState, np.random.Generator, int]
+        ] = None,
         missing: float = np.nan,
         num_parallel_tree: Optional[int] = None,
         monotone_constraints: Optional[Union[Dict[str, int], str]] = None,
@@ -789,6 +791,10 @@ def get_params(self, deep: bool = True) -> Dict[str, Any]:
             params["random_state"] = params["random_state"].randint(
                 np.iinfo(np.int32).max
             )
+        elif isinstance(params["random_state"], np.random.Generator):
+            params["random_state"] = int(
+                params["random_state"].integers(np.iinfo(np.int32).max)
+            )
 
         return params
 
diff --git a/tests/python/test_with_sklearn.py b/tests/python/test_with_sklearn.py
index b40ae67c5dd3..c919a01adb10 100644
--- a/tests/python/test_with_sklearn.py
+++ b/tests/python/test_with_sklearn.py
@@ -702,6 +702,10 @@ def test_sklearn_random_state():
     clf = xgb.XGBClassifier(random_state=random_state)
     assert isinstance(clf.get_xgb_params()['random_state'], int)
 
+    random_state = np.random.default_rng(seed=404)
+    clf = xgb.XGBClassifier(random_state=random_state)
+    assert isinstance(clf.get_xgb_params()['random_state'], int)
+
 
 def test_sklearn_n_jobs():
     clf = xgb.XGBClassifier(n_jobs=1)