diff --git a/imblearn/metrics/_classification.py b/imblearn/metrics/_classification.py
index c7ba87bf2..fa55fd69e 100644
--- a/imblearn/metrics/_classification.py
+++ b/imblearn/metrics/_classification.py
@@ -1131,7 +1131,8 @@ def macro_averaged_mean_absolute_error(y_true, y_pred, *, sample_weight=None):
     mae = []
     for possible_class in labels:
         indices = np.flatnonzero(y_true == possible_class)
-
+        if len(indices) == 0:
+            continue
         mae.append(
             mean_absolute_error(
                 y_true[indices],
@@ -1140,4 +1141,4 @@ def macro_averaged_mean_absolute_error(y_true, y_pred, *, sample_weight=None):
             )
         )
 
-    return np.sum(mae) / len(mae)
+    return np.mean(mae) if mae else 0.0
\ No newline at end of file
diff --git a/imblearn/metrics/tests/test_classification.py b/imblearn/metrics/tests/test_classification.py
index 422c27e57..36f23d65c 100644
--- a/imblearn/metrics/tests/test_classification.py
+++ b/imblearn/metrics/tests/test_classification.py
@@ -547,3 +547,11 @@ def test_macro_averaged_mean_absolute_error_sample_weight():
     )
 
     assert ma_mae_unit_weights == pytest.approx(ma_mae_no_weights)
+def test_macro_averaged_mean_absolute_error_missing_class():
+    # Regression test for issue #1094
+    # Class 1 is missing in y_true, but exists in y_pred
+    y_true = [0, 0]
+    y_pred = [0, 1]
+    # Expected: (MAE for class 0 only) / 1 class = 0.5
+    res = macro_averaged_mean_absolute_error(y_true, y_pred)
+    assert res == 0.5