There are many changes made by technology in human life, especially in banking, where there is a revolution caused with the introduction of automation and machine learning. This project aims at utilizing historical datas with advanced machine learning algorithms for loan approval prediction. Models such as logistic regression, random forests, and support vector machines are used for optimizing prediction accuracies, efficiency, and risk management. This implementation entails data preprocessing techniques as well as their evaluation metrics like accuracy, precision, recall, and F1 score. This study finds the effect of machine learning in loan approval systems and discusses the way forward in the financial domain for further enhancements.