Advanced machine learning techniques, including ensemble learning and explainable AI, are used to improve credit risk prediction. Traditional methods, such as logistic regression, are not capable of handling complex financial data. Extreme Gradient Boosting (XGBoost) model is employed here and along with that “Probability of Default (PD)”, “Exposure at Default (EAD)”, and “Loss Given Default (LGD)” are integrated into a unified predictive framework, using “Ensemble learning” and “Feature engineering”, which aid in enhancing the model’s accuracy and reducing false predictions. Furthermore, the study incorporates “SHAP” values for model interpretability in an effort to meet the demand for transparency. Finally, also “Gradient Boosting Machines (GBM)” and “LightGBM” are designed where the ensemble models improve the credit risk evaluation ability by predicting “high-risk” borrower and computing expected losses, thus enabling better financial decisions. This approach refines credit risk management and also resonates with regulatory compliance like Basel III—having huge implications for loan approvals and risk-weighted asset optimization.