diff --git a/AUTHORS.rst b/AUTHORS.rst
index 5ba2de610..6ef4fb579 100644
--- a/AUTHORS.rst
+++ b/AUTHORS.rst
@@ -52,4 +52,5 @@ Contributors
 * Faustin Pulvéric <faustin.pulveric@gmail.com>
 * Chaoqi Zhang <prncoprs@163.com>
 * Leena Kamran Qidwai
+* Aman Vishnoi <amanvishnoi777@gmail.com>
 To be continued ...
diff --git a/mapie/classification.py b/mapie/classification.py
index c4c92cfef..b29127a23 100644
--- a/mapie/classification.py
+++ b/mapie/classification.py
@@ -965,6 +965,7 @@ def fit(
             X=X,
             sample_weight=sample_weight,
             groups=groups,
+            predict_params=predict_params,
         )
         return self
 
diff --git a/mapie/conformity_scores/sets/raps.py b/mapie/conformity_scores/sets/raps.py
index 30cfb6d00..3ab462656 100644
--- a/mapie/conformity_scores/sets/raps.py
+++ b/mapie/conformity_scores/sets/raps.py
@@ -180,9 +180,15 @@ def get_conformity_scores(
             Conformity scores.
         """
         # Compute y_pred and position on the RAPS validation dataset
-        self.y_pred_proba_raps = self.predictor.single_estimator_.predict_proba(
-            self.X_raps
-        )
+        predict_params = kwargs.pop("predict_params", None)
+        if predict_params is not None and len(predict_params) > 0:
+            self.y_pred_proba_raps = self.predictor.single_estimator_.predict_proba(
+                self.X_raps, **predict_params
+            )
+        else:
+            self.y_pred_proba_raps = self.predictor.single_estimator_.predict_proba(
+                self.X_raps
+            )
         self.position_raps = get_true_label_position(
             self.y_pred_proba_raps, self.y_raps
         )
diff --git a/mapie/estimator/classifier.py b/mapie/estimator/classifier.py
index 5742e668c..20abfdd69 100644
--- a/mapie/estimator/classifier.py
+++ b/mapie/estimator/classifier.py
@@ -384,7 +384,7 @@ def predict_proba_calib(
         check_is_fitted(self, self.fit_attributes)
 
         if self.cv == "prefit":
-            y_pred_proba = self.single_estimator_.predict_proba(X)
+            y_pred_proba = self.single_estimator_.predict_proba(X, **predict_params)
             y_pred_proba = self._check_proba_normalized(y_pred_proba)
         else:
             X = cast(NDArray, X)
diff --git a/mapie/tests/test_classification.py b/mapie/tests/test_classification.py
index a4123c02a..9bf0f62a7 100644
--- a/mapie/tests/test_classification.py
+++ b/mapie/tests/test_classification.py
@@ -1764,6 +1764,42 @@ def test_predict_parameters_passing() -> None:
     np.testing.assert_equal(y_pred, 0)
 
 
+def test_raps_with_predict_params() -> None:
+    """Test that predict_params are correctly passed when using RAPS."""
+    X, y = make_classification(
+        n_samples=500,
+        n_features=10,
+        n_informative=3,
+        n_classes=3,
+        random_state=random_state,
+    )
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=0.2, random_state=random_state
+    )
+    estimator = CustomGradientBoostingClassifier(random_state=random_state)
+    predict_params = {"check_predict_params": True}
+    mapie_clf = _MapieClassifier(
+        estimator=estimator,
+        conformity_score=RAPSConformityScore(size_raps=0.2),
+        cv="split",
+        test_size=0.2,
+        random_state=random_state,
+    )
+
+    mapie_clf.fit(X_train, y_train, predict_params=predict_params)
+
+    y_pred, y_ps = mapie_clf.predict(
+        X_test,
+        alpha=0.1,
+        include_last_label="randomized",
+        agg_scores="mean",
+        **predict_params,
+    )
+    # Ensure the output shapes are correct
+    assert y_pred.shape == (X_test.shape[0],)
+    assert y_ps.shape == (X_test.shape[0], len(np.unique(y)), 1)
+
+
 def test_with_no_predict_parameters_passing() -> None:
     """
     Test passing with no predict parameters from the