We present a comprehensive analysis using an advanced framework to forecast the churning of bank customers. We use a diverse range of machine learning and deep learning models on datasets, both with and without SMOTE. Results show that models trained on data with SMOTE exhibited superior performance compared to models trained on non-SMOTE data. Results also show that among these models, the Light GBM which is a gradient-boosting framework Classifier achieves the highest accuracy rate of 85.16%. Here, we primarily focus on comparing conventional machine-learning models with advanced deep learning approaches. We also find that deep learning models can show promising outcomes but also emphasize the importance of precise model tweaking, particularly in imbalanced datasets. We also employ the significance of variable analysis that indicates age, account balance, and estimated salary as crucial indicators of turnover. Results also indicate valuable insights into customer retention dynamics and emphasize the need for data augmentation approaches such as SMOTE in enhancing the predictive capability of models. Finally, this research enhances the banking sector’s comprehension of churn prediction, providing practical insights for creating focused retention strategies and progressing the field of predictive analytics.