Accurately predicting credit risk is essential for banking institutions as it influences loan decisions and financial stability. Traditional credit evaluation approaches often depend on statistical models that struggle to effectively capture intricate, nonlinear relationships within customer data. This study introduces a machine learning-based credit risk prediction model aimed at improving accuracy and decision-making reliability. The model integrates ensemble learning techniques, such as random forests and gradient boosting, to assess a wide range of financial, demographic, and behavioral factors. Experimental findings indicate that the proposed model effectively detects high-risk customers while minimizing false negatives, demonstrating strong precision and recall. This research offers a data-driven, adaptive solution for credit risk assessment that meets the evolving requirements of contemporary banking systems.