diff --git a/python/src/robyn/modeling/ridge/ridge_data_builder.py b/python/src/robyn/modeling/ridge/ridge_data_builder.py
index ece54ecfa..b4988487e 100644
--- a/python/src/robyn/modeling/ridge/ridge_data_builder.py
+++ b/python/src/robyn/modeling/ridge/ridge_data_builder.py
@@ -2,6 +2,7 @@
 from typing import Any, Dict, Optional, Tuple
 import pandas as pd
 import numpy as np
+from scipy.signal import lfilter
 
 
 class RidgeDataBuilder:
@@ -166,12 +167,11 @@ def _hyper_collector(
         return hyper_collect
 
     def _geometric_adstock(self, x: pd.Series, theta: float) -> pd.Series:
-        # print(f"Before adstock: {x.head()}")
-        y = x.copy()
-        for i in range(1, len(x)):
-            y.iloc[i] += theta * y.iloc[i - 1]
-        # print(f"After adstock: {y.head()}")
-        return y
+
+        x_array = x.values
+        # Use lfilter to efficiently compute the geometric transformation
+        y = lfilter([1], [1, -theta], x_array)
+        return pd.Series(y, index=x.index)
 
     def _hill_transformation(
         self, x: pd.Series, alpha: float, gamma: float
diff --git a/python/tests/unit/modeling/ridge/test_adstock.py b/python/tests/unit/modeling/ridge/test_adstock.py
new file mode 100644
index 000000000..cf9740c9e
--- /dev/null
+++ b/python/tests/unit/modeling/ridge/test_adstock.py
@@ -0,0 +1,31 @@
+import pytest
+import numpy as np
+import pandas as pd
+from scipy.signal import lfilter
+from robyn.modeling.ridge.ridge_data_builder import RidgeDataBuilder
+
+
+def original_geometric_adstock(x: pd.Series, theta: float) -> pd.Series:
+    y = x.copy()
+    for i in range(1, len(x)):
+        y.iloc[i] += theta * y.iloc[i - 1]
+    return y
+
+
+@pytest.mark.parametrize("theta", [0, 0.5, 0.8, 1])
+def test_geometric_adstock(theta):
+    x = pd.Series(np.random.rand(10_000))  # Random test data
+
+    # Instantiate the RidgeDataBuilder object (without requiring real data)
+    dummy_data = None
+    ridge_builder = RidgeDataBuilder(dummy_data, dummy_data)
+
+    # Call the actual function from the RidgeDataBuilder instance
+    optimized = ridge_builder._geometric_adstock(x, theta)
+
+    # Compute the expected output using the original function
+    original = original_geometric_adstock(x, theta)
+
+    assert np.allclose(
+        original, optimized, atol=1e-6
+    ), f"Mismatch found for theta={theta}"