diff --git a/hdbscan/hdbscan_.py b/hdbscan/hdbscan_.py
index 60dddd05..d3bc4917 100644
--- a/hdbscan/hdbscan_.py
+++ b/hdbscan/hdbscan_.py
@@ -4,8 +4,10 @@
          of Applications with Noise
 """
 
+import sklearn
 import numpy as np
 
+from packaging.version import Version
 from sklearn.base import BaseEstimator, ClusterMixin
 from sklearn.metrics import pairwise_distances
 from scipy.sparse import issparse
@@ -37,7 +39,14 @@
 from .plots import CondensedTree, SingleLinkageTree, MinimumSpanningTree
 from .prediction import PredictionData
 
-FAST_METRICS = KDTree.valid_metrics + BallTree.valid_metrics + ["cosine", "arccos"]
+if Version(sklearn.__version__) >= Version("1.3.0"):
+    kdtree_valid_metrics = KDTree.valid_metrics()
+    balltree_valid_metrics = BallTree.valid_metrics()
+else:
+    kdtree_valid_metrics = KDTree.valid_metrics
+    balltree_valid_metrics = BallTree.valid_metrics
+
+FAST_METRICS = kdtree_valid_metrics + balltree_valid_metrics + ["cosine", "arccos"]
 
 # Author: Leland McInnes <leland.mcinnes@gmail.com>
 #         Steve Astels <sastels@gmail.com>
@@ -742,19 +751,19 @@ def hdbscan(
                 _hdbscan_generic
             )(X, min_samples, alpha, metric, p, leaf_size, gen_min_span_tree, **kwargs)
         elif algorithm == "prims_kdtree":
-            if metric not in KDTree.valid_metrics:
+            if metric not in kdtree_valid_metrics:
                 raise ValueError("Cannot use Prim's with KDTree for this" " metric!")
             (single_linkage_tree, result_min_span_tree) = memory.cache(
                 _hdbscan_prims_kdtree
             )(X, min_samples, alpha, metric, p, leaf_size, gen_min_span_tree, **kwargs)
         elif algorithm == "prims_balltree":
-            if metric not in BallTree.valid_metrics:
+            if metric not in balltree_valid_metrics:
                 raise ValueError("Cannot use Prim's with BallTree for this" " metric!")
             (single_linkage_tree, result_min_span_tree) = memory.cache(
                 _hdbscan_prims_balltree
             )(X, min_samples, alpha, metric, p, leaf_size, gen_min_span_tree, **kwargs)
         elif algorithm == "boruvka_kdtree":
-            if metric not in BallTree.valid_metrics:
+            if metric not in balltree_valid_metrics:
                 raise ValueError("Cannot use Boruvka with KDTree for this" " metric!")
             (single_linkage_tree, result_min_span_tree) = memory.cache(
                 _hdbscan_boruvka_kdtree
@@ -771,7 +780,7 @@ def hdbscan(
                 **kwargs
             )
         elif algorithm == "boruvka_balltree":
-            if metric not in BallTree.valid_metrics:
+            if metric not in balltree_valid_metrics:
                 raise ValueError("Cannot use Boruvka with BallTree for this" " metric!")
             if (X.shape[0] // leaf_size) > 16000:
                 warn(
@@ -802,7 +811,7 @@ def hdbscan(
             (single_linkage_tree, result_min_span_tree) = memory.cache(
                 _hdbscan_generic
             )(X, min_samples, alpha, metric, p, leaf_size, gen_min_span_tree, **kwargs)
-        elif metric in KDTree.valid_metrics:
+        elif metric in kdtree_valid_metrics:
             # TO DO: Need heuristic to decide when to go to boruvka;
             # still debugging for now
             if X.shape[1] > 60:
@@ -1237,9 +1246,9 @@ def generate_prediction_data(self):
 
         if self.metric in FAST_METRICS:
             min_samples = self.min_samples or self.min_cluster_size
-            if self.metric in KDTree.valid_metrics:
+            if self.metric in kdtree_valid_metrics:
                 tree_type = "kdtree"
-            elif self.metric in BallTree.valid_metrics:
+            elif self.metric in balltree_valid_metrics:
                 tree_type = "balltree"
             else:
                 warn("Metric {} not supported for prediction data!".format(self.metric))
diff --git a/hdbscan/robust_single_linkage_.py b/hdbscan/robust_single_linkage_.py
index 5662190e..e5c5ec83 100644
--- a/hdbscan/robust_single_linkage_.py
+++ b/hdbscan/robust_single_linkage_.py
@@ -2,8 +2,10 @@
 """
 Robust Single Linkage: Density based single linkage clustering.
 """
+import sklearn
 import numpy as np
 
+from packaging.version import Version
 from sklearn.base import BaseEstimator, ClusterMixin
 from sklearn.metrics import pairwise_distances
 from scipy.sparse import issparse
@@ -24,7 +26,14 @@
 #
 # License: BSD 3 clause
 
-FAST_METRICS = KDTree.valid_metrics + BallTree.valid_metrics
+if Version(sklearn.__version__) >= Version("1.3.0"):
+    kdtree_valid_metrics = KDTree.valid_metrics()
+    balltree_valid_metrics = BallTree.valid_metrics()
+else:
+    kdtree_valid_metrics = KDTree.valid_metrics
+    balltree_valid_metrics = BallTree.valid_metrics
+
+FAST_METRICS = kdtree_valid_metrics + balltree_valid_metrics
 
 
 def _rsl_generic(X, k=5, alpha=1.4142135623730951, metric='euclidean',
@@ -266,7 +275,7 @@ def robust_single_linkage(X, cut, k=5, alpha=1.4142135623730951,
             # We can't do much with sparse matrices ...
             single_linkage_tree = memory.cache(_rsl_generic)(
                 X, k, alpha, metric, **kwargs)
-        elif metric in KDTree.valid_metrics:
+        elif metric in kdtree_valid_metrics:
             # Need heuristic to decide when to go to boruvka;
             # still debugging for now
             if X.shape[1] > 128:
diff --git a/requirements.txt b/requirements.txt
index 487cfe7b..2cc6fd64 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,6 @@
 cython>=0.27
 numpy>=1.20
+packaging
 scipy>= 1.0
 scikit-learn>=0.20
-joblib>=1.0
+joblib>=1.0
\ No newline at end of file