Loan prediction seeks to reduce the lender’s risk by precisely identifying high-risk borrowers who have a higher likelihood of loan default. Loan prediction models can get increased accuracy, robustness, and generalization performance by utilizing ensemble learning approaches. This will help lenders make better decisions and lower the chance of defaults. This research study offers a thorough examination of supervised machine-learning methods used in loan prediction. We investigate the effectiveness of several well-known algorithms, such as Multiple Linear Regression, K- Nearest Neighbor, Support Vector Machines, Random Forest, and Gradient Boosting Machines, by utilizing a synthetic dataset that includes a variety of demographic, financial, and credit-related characteristics.