This study compares the performance of statistical models and various machine learning methods in predicting corporate bankruptcy using financial and macroeconomic data from publicly listed companies. It also analyzes the effectiveness of oversampling methods in addressing the class imbalance problem. The empirical analysis employs logistic regression, random forest, XGBoost (extreme gradient boosting), and deep neural networks, along with oversampling techniques such as SMOTE (synthetic minority over-sampling technique) and ADASYN (adaptive synthetic sampling). The results show that XGBoost delivers the most accurate and balanced predictive performance across both the original dataset and the oversampled datasets. In contrast, logistic regression exhibits high recall but limited practical applicability due to its low precision. These findings suggest that combining oversampling techniques with machine learning models such as XGBoost provides a more effective and practical approach to bankruptcy prediction in the context of imbalanced data.