Credit default prediction is a critical task in the financial industry, aiming to assess the likelihood of customers defaulting on their credit obligations. In this paper, we present a novel ensemble modeling approach utilizing stacking methodology to address the challenge of credit default prediction. Despite the availability of extensive data, accurately identifying default risks remains a complex and crucial task for financial institutions. Our proposed ensemble model combines the strengths of three powerful algorithms: XGBoost, LightGBM, and CatBoost, leveraging their individual predictive capabilities to enhance overall performance. Through rigorous experimentation and evaluation on a comprehensive dataset comprising timeseries behavioral data and customer profiles, we demonstrate the effectiveness of our approach in achieving superior predictive accuracy.