Artificial intelligence (AI) has had a significant impact on a wide range of industries, including robots, e-commerce, healthcare, and banking. The wealth of personal data has made it possible to convert human civilization's knowledge into digital form, which in turn has made it possible to use machine learning to create new knowledge and behaviors. AI has demonstrated significant promise for creating customer relationship management (CRM)/Enterprise Resource Planning (ERP) system retention strategies. AI is capable of analyzing consumer data to forecast habits, preferences, and churn risks through the use of sophisticated algorithms and machine learning. By anticipating client wants, personalizing interactions, and providing incentives that are specifically tailored, organizations can cultivate long-term consumer loyalty. AI can also enable real-time adaptation in CRM/ERP systems, improving customer satisfaction and increasing retention rates. This paper presents a Systematic Literature Review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses model for AI-based customer churn prediction in several industries and proposes a predictive analysis model of churn using a hybrid ensemble supervised machine learning algorithm for an online subscription service. The results are quite satisfactory with accuracy of 82%, precision of 78%, recall of 82%, specificity of 82%, and F1 score of 75%. The paper further discusses AI-driven customer retention policies’ opportunities, issues, and challenges.