Customer retention drives sustainable growth in the competitive landscape of subscription-based service platforms. This study presents an AI-driven predictive modeling framework designed to enhance churn prediction accuracy and enable tailored retention strategies. Using the publicly available “Customer Churn Prediction” dataset, which includes customer demographics, account details, and behavioral metrics, the model leverages gated recurrent units (GRUs) for sequential data handling, excelling in capturing long-term dependencies and temporal patterns. This transparent and agile model addresses the limitations of conventional deep learning methods by providing actionable insights that can be immediately applied to reduce churn rates. Comprehensive testing across multiple datasets validates the robustness and adaptability of the framework, consistently achieving superior results. Notably, the Python-based model achieved 98% accuracy, surpassing CNN-LSTM approaches in predictive performance. The framework integrates optimal data preprocessing, feature selection, and advanced machine learning techniques to ensure high precision in identifying potential customer churn. By utilizing behavioral and engagement metrics relevant to subscription services, the model provides key insights into customer satisfaction and engagement trends. This study contributes a high-performance, scalable solution for subscription-based industries, empowering organizations to enhance customer relationship management, foster loyalty, minimize turnover, and deliver personalized user experiences to improve satisfaction and retention.