This study addresses the critical issue of customer churn in the telecommunications industry, a sector where high churn rates can significantly impact revenue and growth. The research aims to develop effective retention strategies by understanding the factors leading to customer churn. Advanced machine learning models, including Naive Bayes, Random Forest, AdaBoost, XGBoost, and LightGBM, are applied to predict customer churn using a fictional dataset from a telecommunications company. The study emphasizes the importance of model interpretability, particularly through Shapley Additive exPlanations (SHAP), to ensure trust and transparency in decision-making. LightGBM is found to be the most effective model, achieving an accuracy of 80.70%, precision of 84.35%, recall of 90.54%, and an F1-score of 87.34%. SHAP analysis reveals that features such as tenure, contract type, and monthly charges are significant predictors of customer churn. These findings provide telecom companies with actionable insights to tailor retention strategies, improve customer satisfaction, and enhance loyalty. The study highlights the importance of combining predictive analytics with interpretability methods to gain deeper insights into customer behavior. Future research should focus on validating these findings with real-world data, exploring more sophisticated models, and incorporating temporal dynamics to further enhance churn prediction models' predictive power and applicability.This study addresses the critical issue of customer churn in the telecommunications industry, a sector where high churn rates can significantly impact revenue and growth. The research aims to develop effective retention strategies by understanding the factors leading to customer churn. Advanced machine learning models, including Naive Bayes, Random Forest, AdaBoost, XGBoost, and LightGBM, are applied to predict customer churn using a fictional dataset from a telecommunications company. The study emphasizes the importance of model interpretability, particularly through Shapley Additive exPlanations (SHAP), to ensure trust and transparency in decision-making. LightGBM is found to be the most effective model, achieving an accuracy of 80.70%, precision of 84.35%, recall of 90.54%, and an F1-score of 87.34%. SHAP analysis reveals that features such as tenure, contract type, and monthly charges are significant predictors of customer churn. These findings provide telecom companies with actionable insights to tailor retention strategies, improve customer satisfaction, and enhance loyalty. The study highlights the importance of combining predictive analytics with interpretability methods to gain deeper insights into customer behavior. Future research should focus on validating these findings with real-world data, exploring more sophisticated models, and incorporating temporal dynamics to further enhance churn prediction models' predictive power and applicability.