diff --git a/keras/src/saving/saving_lib.py b/keras/src/saving/saving_lib.py
index e73390c28ea3..db7f702a87ec 100644
--- a/keras/src/saving/saving_lib.py
+++ b/keras/src/saving/saving_lib.py
@@ -866,6 +866,30 @@ def _load_state(
             error_msgs.pop(id(saveable))
 
 
+def _get_container_item_name(saveable):
+    """Get the name to use for a saveable in a container.
+
+    Uses `saveable.name` for stable topology-based matching. This ensures
+    that layers with the same name but different classes (e.g., a custom
+    LSTM subclass vs vanilla LSTM) map to the same path in the weights
+    file. Falls back to the class name for saveables without a name.
+    """
+    if hasattr(saveable, "name") and isinstance(saveable.name, str):
+        return saveable.name
+    return naming.to_snake_case(saveable.__class__.__name__)
+
+
+def _store_has_path(weights_store, path):
+    """Check if a path exists in the weights store."""
+    if weights_store is None or not path:
+        return False
+    if isinstance(weights_store, H5IOStore):
+        return path in weights_store.h5_file
+    if isinstance(weights_store, NpzIOStore):
+        return path in weights_store.contents
+    return True
+
+
 def _save_container_state(
     container, weights_store, assets_store, inner_path, visited_saveables
 ):
@@ -877,10 +901,7 @@ def _save_container_state(
 
     for saveable in container:
         if isinstance(saveable, KerasSaveable):
-            # Do NOT address the saveable via `saveable.name`, since
-            # names are usually autogenerated and thus not reproducible
-            # (i.e. they may vary across two instances of the same model).
-            name = naming.to_snake_case(saveable.__class__.__name__)
+            name = _get_container_item_name(saveable)
             if name in used_names:
                 used_names[name] += 1
                 name = f"{name}_{used_names[name]}"
@@ -908,22 +929,43 @@ def _load_container_state(
     from keras.src.saving.keras_saveable import KerasSaveable
 
     used_names = {}
+    used_class_names = {}
     if isinstance(container, dict):
         container = list(container.values())
 
     for saveable in container:
         if isinstance(saveable, KerasSaveable):
-            name = naming.to_snake_case(saveable.__class__.__name__)
+            name = _get_container_item_name(saveable)
             if name in used_names:
                 used_names[name] += 1
                 name = f"{name}_{used_names[name]}"
             else:
                 used_names[name] = 0
+            candidate_path = file_utils.join(inner_path, name).replace(
+                "\\", "/"
+            )
+
+            # Backward compatibility: if the name-based path doesn't
+            # exist in the store, fall back to class-name-based path
+            # (the format used before this fix).
+            if not _store_has_path(weights_store, candidate_path):
+                class_name = naming.to_snake_case(saveable.__class__.__name__)
+                if class_name in used_class_names:
+                    used_class_names[class_name] += 1
+                    class_name = f"{class_name}_{used_class_names[class_name]}"
+                else:
+                    used_class_names[class_name] = 0
+                fallback_path = file_utils.join(inner_path, class_name).replace(
+                    "\\", "/"
+                )
+                if _store_has_path(weights_store, fallback_path):
+                    candidate_path = fallback_path
+
             _load_state(
                 saveable,
                 weights_store,
                 assets_store,
-                inner_path=file_utils.join(inner_path, name).replace("\\", "/"),
+                inner_path=candidate_path,
                 skip_mismatch=skip_mismatch,
                 visited_saveables=visited_saveables,
                 failed_saveables=failed_saveables,
diff --git a/keras/src/saving/saving_lib_test.py b/keras/src/saving/saving_lib_test.py
index 59f7c3473aed..862a930fb0a1 100644
--- a/keras/src/saving/saving_lib_test.py
+++ b/keras/src/saving/saving_lib_test.py
@@ -1145,6 +1145,44 @@ def is_remote_path(path):
             model.save_weights(temp_filepath)
             model.load_weights(temp_filepath)
 
+    def test_custom_subclass_weight_loading(self):
+        """Test that weights saved from a custom subclass can be loaded
+        into a model using the base class, and vice versa, when the layer
+        names match (issue #20322)."""
+
+        @keras.saving.register_keras_serializable(package="custom_lstm_test")
+        class CustomLSTM(keras.layers.LSTM):
+            pass
+
+        # Build model with custom LSTM subclass
+        inputs_a = keras.Input(shape=(10, 1))
+        lstm_a = CustomLSTM(32, name="my_lstm")
+        dense_a = keras.layers.Dense(1, name="output")
+        model_a = keras.Model(
+            inputs_a, dense_a(lstm_a(inputs_a)), name="model_a"
+        )
+
+        # Build model with vanilla LSTM (same layer name)
+        inputs_b = keras.Input(shape=(10, 1))
+        lstm_b = keras.layers.LSTM(32, name="my_lstm")
+        dense_b = keras.layers.Dense(1, name="output")
+        model_b = keras.Model(
+            inputs_b, dense_b(lstm_b(inputs_b)), name="model_b"
+        )
+
+        # Save weights from custom subclass model
+        temp_filepath = os.path.join(
+            self.get_temp_dir(), "custom_lstm.weights.h5"
+        )
+        model_a.save_weights(temp_filepath)
+
+        # Load into vanilla LSTM model
+        model_b.load_weights(temp_filepath)
+
+        # Verify predictions match
+        x = np.random.random((1, 10, 1)).astype("float32")
+        self.assertAllClose(model_a(x), model_b(x))
+
 
 class SavingH5IOStoreTest(testing.TestCase):
     def test_h5_io_store_basics(self):