diff --git a/python-package/lightgbm/basic.py b/python-package/lightgbm/basic.py
index 1db55385af1b..0f2e3697f6ec 100644
--- a/python-package/lightgbm/basic.py
+++ b/python-package/lightgbm/basic.py
@@ -1291,10 +1291,7 @@ def __inner_predict_np2d(
         predict_type: int,
         preds: Optional[np.ndarray],
     ) -> Tuple[np.ndarray, int]:
-        if mat.dtype == np.float32 or mat.dtype == np.float64:
-            data = np.asarray(mat.reshape(mat.size), dtype=mat.dtype)
-        else:  # change non-float data to float data, need to copy
-            data = np.array(mat.reshape(mat.size), dtype=np.float32)
+        data, layout = _np2d_to_np1d(mat)
         ptr_data, type_ptr_data, _ = _c_float_array(data)
         n_preds = self.__get_num_preds(
             start_iteration=start_iteration,
@@ -1314,7 +1311,7 @@ def __inner_predict_np2d(
                 ctypes.c_int(type_ptr_data),
                 ctypes.c_int32(mat.shape[0]),
                 ctypes.c_int32(mat.shape[1]),
-                ctypes.c_int(_C_API_IS_ROW_MAJOR),
+                ctypes.c_int(layout),
                 ctypes.c_int(predict_type),
                 ctypes.c_int(start_iteration),
                 ctypes.c_int(num_iteration),
diff --git a/tests/python_package_test/test_engine.py b/tests/python_package_test/test_engine.py
index cb2e893c9612..05afddb77c77 100644
--- a/tests/python_package_test/test_engine.py
+++ b/tests/python_package_test/test_engine.py
@@ -4611,3 +4611,18 @@ def test_bagging_by_query_in_lambdarank():
     ndcg_score_no_bagging_by_query = gbm_no_bagging_by_query.best_score["valid_0"]["ndcg@5"]
     assert ndcg_score_bagging_by_query >= ndcg_score - 0.1
     assert ndcg_score_no_bagging_by_query >= ndcg_score - 0.1
+
+
+def test_equal_predict_from_row_major_and_col_major_data():
+    X_row, y = make_synthetic_regression()
+    assert X_row.flags["C_CONTIGUOUS"] and not X_row.flags["F_CONTIGUOUS"]
+    ds = lgb.Dataset(X_row, y)
+    params = {"num_leaves": 8, "verbose": -1}
+    bst = lgb.train(params, ds, num_boost_round=5)
+    preds_row = bst.predict(X_row)
+
+    X_col = np.asfortranarray(X_row)
+    assert X_col.flags["F_CONTIGUOUS"] and not X_col.flags["C_CONTIGUOUS"]
+    preds_col = bst.predict(X_col)
+
+    np.testing.assert_allclose(preds_row, preds_col)