<p>This study aims to improve customer churn prediction by integrating machine learning algorithms and evaluating their performance using criteria like accuracy, profit, and Customer Lifetime Value (CLV). Using a Telecom Customer Churn Prediction dataset, the study analyzes the efficiency of various models such as XGBoost, Linear Regression, Decision Tree Regressor, Lasso, Random Forest, and Gradient Boosting Regressor. This study introduces an Adaptive Profit-Centric Churn Prediction Engine (APCPE), an innovative churn prediction system that evaluates profitability and adapts to changing customer behavior. Each model’s accuracy, profit, and CLV are assessed, revealing that the proposed APCPE is the most accurate model, with a 97.01% accuracy rate, a profit of $606.3125, and a CLV of $1212.625. Convergence curves, including R2 convergence curves, show the model’s performance and convergence rates for each algorithm. Convergence curves, including R2 convergence curves, show the model’s performance and convergence rates for each algorithm. The study digs deeper into interdependence modeling, which involves finding and measuring consumer interactions or dependencies such as social network linkages and product co-occurrence. The study adds insights into modeling customer behavior dependencies and their impact on churn prediction, providing a holistic approach to improving corporate tactics for customer retention.</p>