diff --git a/imblearn/metrics/_regression.py b/imblearn/metrics/_regression.py
new file mode 100644
index 000000000..a9de8ecb2
--- /dev/null
+++ b/imblearn/metrics/_regression.py
@@ -0,0 +1,26 @@
+import numpy as np
+
+def macro_mean_squared_error(y_true, y_pred):
+    """
+    Compute the macro-averaged mean squared error.
+
+    Parameters
+    ----------
+    y_true : array-like of shape (n_samples, n_outputs)
+        True values.
+    y_pred : array-like of shape (n_samples, n_outputs)
+        Predicted values.
+
+    Returns
+    -------
+    float
+        Macro-averaged MSE over all outputs.
+    """
+    y_true = np.array(y_true)
+    y_pred = np.array(y_pred)
+
+    # Compute MSE for each output
+    mse_per_output = np.mean((y_true - y_pred) ** 2, axis=0)
+
+    # Return macro-average
+    return np.mean(mse_per_output)
diff --git a/imblearn/tests/test_regression.py b/imblearn/tests/test_regression.py
new file mode 100644
index 000000000..7807478b3
--- /dev/null
+++ b/imblearn/tests/test_regression.py
@@ -0,0 +1,10 @@
+import numpy as np
+from imblearn.metrics._regression import macro_mean_squared_error
+
+def test_macro_mse():
+    y_true = np.array([[1, 2], [3, 4]])
+    y_pred = np.array([[1, 1], [4, 4]])
+    
+    result = macro_mean_squared_error(y_true, y_pred)
+    expected = np.mean([np.mean([0,1]), np.mean([1,0])])  # manual calculation
+    assert np.isclose(result, expected)