Loans constitute highly profitable assets for banks and financial institutions, yielding substantial returns through interest accumulated from an expanding customer base. Nonetheless, such profitability is accompanied by significant risks, as loans are susceptible to default, wherein certain borrowers may fail to meet their financial obligations. High levels of non-performing loans thus pose considerable risks to the stability of the banking sector, potentially leading to financial instability and insolvency. As such, a critical aspect of loan approval processes involves accurately assessing a borrower’s capacity to repay. Advances in machine learning have introduced valuable tools for this purpose, enabling banks to classify borrowers into defaulters and non-defaulters by analyzing personal and transactional data. This paper presents a comprehensive pedagogical exploration of the role of machine learning techniques in loan default classification, presenting both the findings of our study and demonstrating the potential of machine learning classifiers to predict repayment likelihood, thereby allowing banks to mitigate risk more effectively. To this end, we compare four machine learning algorithms—Random Forest, Adaptive Boosting, Extreme Gradient Boosting, and Stacking—each trained using ensemble learning techniques. Among these, Stacking proved the most effective, achieving a sensitivity of 90.4%, an AUC of 0.9486, and an accuracy of 94.6%, indicating its superior capability for accurately categorizing loan applicants as likely defaulters or non-defaulters.