One of the important tasks in banking is the prediction of customer churn which has a direct relation toward customer retention and bank’s profitability. As such, this study proposes an integrated approach using voting classifiers to improve on the groups of churn prediction models. The first base classifiers include Support Vector Machines-K-Nearest Neighbors (SVM-KNN), Support Vector Machines-Decision Forest (SVM-DF), Decision Forest-Random Forest (DF-RF), Random Forest-Naive Bayes (RF-NB), and Extra Trees-Random Forest (ET-RF). It is shown that the proposed method that incorporates both hard and soft voting strategies outperforms the individual models in terms of both, the predictive accuracy and model integrity. New experimental outcomes on a real banking dataset show a substantial improvement in the ability to clearly identify such customers, which can be beneficial for retention management activities. Supporting the above-stated research objectives, the study establishes that ensemble learning possesses a significant capability of handling the challenging customer churn issue in the steadily evolving banking sector.