This paper analyses how machine learning methods can be used to optimize credit risk modeling and default prediction in financial institutions. The first one is to overcome the weakness of conventional statistical credit rating models to capture nonlinearities and high-dimensional borrower data. The research design of the study is the quantitative research design based on supervised machine learning models, such as logistic regression, random forest, support vector machines, and gradient boosting techniques applied to financial and behavioral variables of borrowers on an individual basis. Accuracy, Area Under the Curve (AUC ), precision, recall, and default classification error rates are used to measure performance using the model. Findings show that the ensemble-based models are better than the traditional models, and the gradient boosting has an AUC of 0.89 versus 0.74 with logistic regression, as well as a decrease in the percentage of the misclassification by a factor of about 21. The analysis of the feature importance shows that the debt-to-income ratio, payment history, and credit utilization are the main predictors of default. The results indicate that machine learning models have a great role to play in predictive accuracy and risk discrimination. It is found that machine learning can be effectively applied to the credit risk frameworks to reduce the default risks, optimize lending processes, and ensure financial stability when accompanied by proper governance and a model risk management practice..