Understanding and predicting customer behavior is essential for sustaining growth and profitability in the software market. This study proposes an AI-driven framework for analyzing customer adoption patterns and churn risk through the integration of clustering and classification techniques. Using real-world behavioral data, the research first applies K-Means clustering to segment users into distinct behavioral groups. Four meaningful segments were identified, each characterized by varying engagement levels, contract types, and churn tendencies. Subsequently, supervised machine learning models—Logistic Regression, Random Forest, and XGBoost—were employed to predict churn. Among these, XGBoost achieved the highest performance, with an accuracy of 87.1% and an ROC-AUC score of 0.91. Feature importance analysis highlighted tenure, contract type, and monthly charges as critical churn predictors. The findings offer practical insights into personalized retention strategies and pricing interventions, emphasizing the utility of AI in customer analytics. This work contributes to the field by bridging behavioral segmentation with predictive modeling, providing a scalable and interpretable approach to managing customer lifecycle challenges in software-based services.