diff --git a/feature_engine/imputation/missing_indicator.py b/feature_engine/imputation/missing_indicator.py
index 7976aa749..01660a654 100644
--- a/feature_engine/imputation/missing_indicator.py
+++ b/feature_engine/imputation/missing_indicator.py
@@ -160,9 +160,13 @@ def transform(self, X: pd.DataFrame) -> pd.DataFrame:
         """
 
         X = self._transform(X)
-
-        indicator_names = [f"{feature}_na" for feature in self.variables_]
-        X[indicator_names] = X[self.variables_].isna().astype(int)
+        X_indicators = (
+            X[self.variables_]
+            .isna()
+            .astype("int8")
+            .add_suffix("_na")
+        )
+        X = pd.concat([X, X_indicators], axis=1)
 
         return X
 
diff --git a/tests/test_imputation/test_missing_indicator.py b/tests/test_imputation/test_missing_indicator.py
index d5340c1ac..a7f6e9f7c 100644
--- a/tests/test_imputation/test_missing_indicator.py
+++ b/tests/test_imputation/test_missing_indicator.py
@@ -1,4 +1,8 @@
+import warnings
+import numpy as np
+import pandas as pd
 import pytest
+
 from sklearn.pipeline import Pipeline
 
 from feature_engine.imputation import AddMissingIndicator
@@ -96,3 +100,26 @@ def test_get_feature_names_out_from_pipeline(df_na):
 
     assert tr.get_feature_names_out(input_features=None) == feat_out
     assert tr.get_feature_names_out(input_features=original_features) == feat_out
+
+
+def test_no_performance_warning_with_many_variables():
+    n_cols = 101
+    df = pd.DataFrame(
+        np.random.randn(10, n_cols),
+        columns=[f"col_{i}" for i in range(n_cols)],
+    )
+
+    # Introduce missing values
+    df.iloc[0, :] = np.nan
+
+    ami = AddMissingIndicator(missing_only=False)
+    ami.fit(df)
+
+    with warnings.catch_warnings(record=True) as captured:
+        warnings.simplefilter("always")
+        ami.transform(df)
+
+    assert not any(
+        issubclass(w.category, pd.errors.PerformanceWarning)
+        for w in captured
+    ), "PerformanceWarning was raised during transform"