The quality of the software is a critical success factor in today’s societies, given that software systems control important sectors such as health, finance, and airlines. This is because identifying errors ahead of time is crucial to a software’s success and keeping maintainability reasonable. There is a high probability of detecting these defects using conventional approaches, but the problem is that the approaches cannot work on large-scale data sets, and their accuracy is questionable. The XGBoost, GB, RF, LR, SVM, and the recently introduced deep learning-based TabNet and Voting Classifier models are compared in this study. The Unified Bug Dataset is used to measure the models’ performance metrics, including precision, accuracy, recall, F1-score, and AUC. Their findings show that XGBoost was the best algorithm, with 93.17% accuracy and 92.98% F1 score, followed by the Voting Classifier with 92.53% accuracy. As for TabNet, a network exclusively for tabular data, the model had an accuracy of 90.47% while providing interpretability and feature selection. The comparative analysis reveals the benefits and drawbacks of models to address the research objectives, and ensemble methods have shown high potential in improving defect prediction accuracy.