diff --git a/doc/whats_new/v0.15.rst b/doc/whats_new/v0.15.rst
index 7ef7284b8..30b944b27 100644
--- a/doc/whats_new/v0.15.rst
+++ b/doc/whats_new/v0.15.rst
@@ -11,6 +11,14 @@ Changelog
 Bug fixes
 .........
 
+- Fix a bug in :func:`~imblearn.metrics.macro_averaged_mean_absolute_error`
+  where a ``ValueError`` was raised when a class was present in ``y_pred``
+  but absent from ``y_true``. Such classes are now skipped (per-class MAE
+  is undefined when there are no ground-truth samples), matching the
+  graceful handling of missing labels in :func:`sklearn.metrics.f1_score`
+  with ``average='macro'``.
+  :pr:`1172` by :user:`jbbqqf`.
+
 Enhancements
 ............
 
diff --git a/imblearn/metrics/_classification.py b/imblearn/metrics/_classification.py
index c7ba87bf2..5c2282a50 100644
--- a/imblearn/metrics/_classification.py
+++ b/imblearn/metrics/_classification.py
@@ -1127,7 +1127,12 @@ def macro_averaged_mean_absolute_error(y_true, y_pred, *, sample_weight=None):
     else:
         sample_weight = np.ones(y_true.shape)
     check_consistent_length(y_true, y_pred, sample_weight)
-    labels = unique_labels(y_true, y_pred)
+    # Iterate only over classes present in y_true: per-class MAE is undefined
+    # for a class that has zero ground-truth samples (mean_absolute_error
+    # rejects empty arrays). This matches the metric's documented intent
+    # ("computes each MAE for each class") and aligns the macro behaviour
+    # with sklearn's f1_score(average='macro') on labels missing from y_true.
+    labels = unique_labels(y_true)
     mae = []
     for possible_class in labels:
         indices = np.flatnonzero(y_true == possible_class)
diff --git a/imblearn/metrics/tests/test_classification.py b/imblearn/metrics/tests/test_classification.py
index 2a022e271..645d90a77 100644
--- a/imblearn/metrics/tests/test_classification.py
+++ b/imblearn/metrics/tests/test_classification.py
@@ -548,3 +548,22 @@ 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_class_only_in_y_pred():
+    # Non-regression test for #1094: when a label appears only in y_pred
+    # (predicted but absent from ground truth), the metric must not raise.
+    # The per-class MAE for a class with zero ground-truth samples is
+    # undefined and is therefore excluded from the macro average — this
+    # matches the docstring intent ("computes each MAE for each class") and
+    # mirrors the graceful handling of absent labels by sklearn's
+    # f1_score(average='macro').
+    # y_true = [0, 0], y_pred = [0, 1]: only class 0 is in y_true, with
+    # samples [0, 0] vs predictions [0, 1] -> MAE = (0+1)/2 = 0.5; macro
+    # over the single ground-truth class = 0.5. Crucially, no exception.
+    assert macro_averaged_mean_absolute_error([0, 0], [0, 1]) == pytest.approx(0.5)
+
+    # Symmetric sanity check: when a label is missing from y_pred but
+    # present in y_true, we still compute MAE on each ground-truth slice.
+    # class 0: |0-0|=0; class 1: |1-0|=1; macro = (0+1)/2 = 0.5.
+    assert macro_averaged_mean_absolute_error([0, 1], [0, 0]) == pytest.approx(0.5)