diff --git a/python-package/lightgbm/basic.py b/python-package/lightgbm/basic.py
index ee55b642ffa0..933768afaf34 100644
--- a/python-package/lightgbm/basic.py
+++ b/python-package/lightgbm/basic.py
@@ -1192,10 +1192,16 @@ def predict(
                 predict_type=predict_type,
             )
         elif isinstance(data, list):
-            try:
-                data = np.array(data)
-            except BaseException as err:
-                raise ValueError("Cannot convert data list to numpy array.") from err
+            if isinstance(data[0], Sequence):
+                try:
+                    data = np.concatenate([i[:] for i in data])
+                except BaseException as err:
+                    raise ValueError('Cannot convert Sequence list to numpy array.') from err
+            else:
+                try:
+                    data = np.array(data)
+                except BaseException as err:
+                    raise ValueError('Cannot convert data list to numpy array.') from err
             preds, nrow = self.__pred_for_np2d(
                 mat=data,
                 start_iteration=start_iteration,
diff --git a/tests/python_package_test/test_basic.py b/tests/python_package_test/test_basic.py
index 7177623be02d..932a89107992 100644
--- a/tests/python_package_test/test_basic.py
+++ b/tests/python_package_test/test_basic.py
@@ -217,6 +217,43 @@ def test_sequence_get_data(num_seq):
     np.testing.assert_array_equal(subset_data.get_data(), X[sorted(used_indices)])
 
 
+def test_retrain_list_of_sequence():
+    X, y = load_breast_cancer(return_X_y=True)
+    seqs = _create_sequence_from_ndarray(X, 2, 100)
+
+    seq_ds = lgb.Dataset(seqs, label=y, free_raw_data=False)
+
+    assert sum([len(s) for s in seq_ds.get_data()]) == X.shape[0]
+    assert len(seq_ds.get_feature_name()) == X.shape[1]
+    assert seq_ds.get_data() == seqs
+
+    params = {
+        "objective": "binary",
+        "num_boost_round": 20,
+        "min_data": 10,
+        "num_leaves": 10,
+        "verbose": -1,
+    }
+
+    model1 = lgb.train(
+        params,
+        seq_ds,
+        keep_training_booster=True,
+    )
+
+    assert model1.current_iteration() == 20 
+    assert model1.num_trees() == 20 
+
+    model2 = lgb.train(
+        params,
+        seq_ds,
+        init_model=model1,
+    )
+
+    assert model2.current_iteration() == 20 
+    assert model2.num_trees() == 20
+
+
 def test_chunked_dataset():
     X_train, X_test, y_train, y_test = train_test_split(
         *load_breast_cancer(return_X_y=True), test_size=0.1, random_state=2