This study aims to predict customer churn in the telecom industry by utilizing complex machine learning (ML) algorithms that give equal importance to high accuracy and model interpretability. Customer churn is a significant challenge for telecom operators, often causing significant revenue loss. ML approaches such as deep learning (DL) and ensemble models can produce very accurate churn predictions but usually suffer from a lack of interpretability, which reduces confidence and optimal decision-making capability. Therefore, this study has incorporated Explainable AI (XAI) techniques, specifically Shapley Additive Explanations (SHAP), to make the model more transparent. SHAP helps provide a deeper insight into what influences churn predictions, which helps telecom companies understand why their customers are leaving. Various models have been explored along with handling data imbalance by using oversampling and undersampling techniques. This study combines prediction accuracy with interpretability to serve as a practical solution for developing more effective retention tactics from telecom organizations, thereby building customer lifetime value and diminishing churn.