Credit refers to borrowers’ ability to obtain goods, services, or funds from a lender with a promise to repay later. Traditional credit scoring models fail to capture complex and nonlinear patterns in financial and borrower data, while machine learning (ML) models, although effective, face challenges such as class imbalance and limited interpretability. This study evaluates the effectiveness of ensemble learning models, such as Voting, Bagging, Boosting, and Stacking, in improving predictive performance compared to individual ML models including Logistic Regression (LR), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbour (KNN), Support Vector Classifier (SVC), and Gradient Boosting Classifier (GBC). To address class imbalance in the Taiwan credit card default dataset, resampling techniques like Synthetic Minority Over-sampling Technique (SMOTE) and the Edited Nearest Neighbour (ENN) are applied. The performance of each model is assessed using standard classification metrics, including accuracy, Area Under the Curve (AUC) in the Receiver Operating Characteristic (ROC) curve, precision, recall, F1-score, area ratio, and k-fold cross-validation (CV) score. Additionally, this study explores methods to enhance the interpretability of the best-performing ensemble model.