Banks play a pivotal role in the economy by providing loans to individuals and businesses. However, the risk associated with loan defaults presents a significant challenge to financial institutions. To address this issue, leveraging the power of Machine Learning (ML) has emerged as a promising solution. In this research paper, we present a comprehensive analysis and a comparative study of various ML algorithms to predict loan defaulters using a large-scale “Loan Default Dataset” obtained from Kaggle. The dataset comprises multiple deterministic factors, including borrower’s income, gender, loan purpose, and more. However, the dataset is not without challenges, as it exhibits strong multicollinearity and contains missing values. Our research encompasses thorough data preprocessing techniques to address these issues, ensuring the dataset’s reliability and suitability for our analyses. Through extensive feature engineering, we identify and select relevant predictors to feed into our classification models. Our investigation encompasses a diverse range of algorithms, including ensemble methods, Decision Trees, Neural Networks, and various types of Naive Bayes classifiers. Remarkably, our results indicate exceptionally high accuracy scores for many algorithms, with a range of models achieving 100% accuracy. However, we caution against potential overfitting or data leakage and emphasize the importance of assessing precision, recall, and F1-score to provide a more comprehensive view of model performance. The results of our research demonstrate the efficacy of ML algorithms in predicting loan defaulters.