The banking sector plays a vital role in the global financial system by providing loans to individuals and businesses for various purposes. While loans generate significant revenue through interest, there is always a risk that borrowers may fail to repay the loan, resulting in financial losses for lending institutions. Therefore, accurately predicting the risk level associated with a loan application is an important task for banks and financial organizations. Traditional loan approval processes rely heavily on manual analysis of customer information, which can be time-consuming and prone to human bias. With the advancement of machine learning techniques, automated systems can now analyse large amounts of financial data to support more efficient and accurate loan approval decisions. This study proposes a machine learning-based loan risk prediction system that analyses customer personal and financial attributes to determine the likelihood of loan default. The dataset used for this study contains multiple features commonly included in loan applications, such as credit history, checking account status, loan amount, employment status, and age of the applicant. Data preprocessing techniques including outlier removal, categorical encoding, and feature scaling are applied to prepare the dataset for model training. Several machine learning algorithms are implemented and compared, including Decision Tree, Random Forest, Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Naive Bayes, and a Stacking Ensemble model. The models are evaluated using performance metrics such as accuracy, precision, recall, F1-score, and confusion matrix analysis. Experimental results demonstrate that ensemble-based approaches provide improved predictive performance compared to individual machine learning models. The proposed system can assist financial institutions in making faster and more reliable loan approval decisions by identifying high-risk applicants before granting loans. By leveraging machine learning techniques, the system enhances the efficiency of credit risk assessment and supports more effective financial decision-making in the banking industry.