<p>Mortgage default prediction is a critical component of credit risk management in the financial sector. Traditional statistical methods, such as logistic regression, often fail to capture complex borrower behavior and adapt to rapidly changing economic conditions. In this study, we present a robust machine learning framework that employs advanced ensemble methods, including LightGBM, XGBoost, Gradient Boosting, AdaBoost, Random Forest, and Extra Trees to enhance the prediction of mortgage delinquencies and foreclosures. A novel contribution of this work is the development of three specialized models targeting distinct stages of mortgage distress: the Early Alert Analysis Model (30–89 days delinquent), the Critical Risk Assessment Model (90 + days delinquent), and the Foreclosure Predictor Model. The models are trained and validated using a comprehensive dataset of 44,168 U.S. mortgage records, enriched with over 100 features from the FHFA NMDB^® and macroeconomic indicators from the Federal Reserve Economic Data (FRED). Leveraging automated machine learning (AutoML) via PyCaret and advanced feature engineering techniques, our models achieve exceptional predictive performance, with AUC scores reaching up to 0.9998. The results demonstrate the superiority of machine learning approaches over traditional models in identifying early-stage risk, severe delinquencies, and imminent foreclosures. This research provides actionable insights for financial institutions and highlights the value of integrating adaptive, interpretable ML systems into modern mortgage risk assessment frameworks.</p>