The aim of this study is to use machine learning to automate and optimize the loan approval process and address the need for timely and effective support for students in India. Using machine learning algorithms, we aim to develop predictive models based on a comprehensive analysis of student demographic, social, and financial data to evaluate the likelihood of approval of loans. We use various machine learning algorithms such as logistic regression, decision trees, random forests, neural networks, and support vector machines (SVM) to predict loan approvals, with a special focus on neural networks. As neural networks did not perform well on loan defaults in several studies, they performed pretty well on the loan approval task. The effectiveness of these models is compared with accuracy, F1 score, and other relevant metrics to determine the most effective approach.