In the software as a service (SaaS) sector, churn is a crucial indicator as it directly affects a business’s earnings, prospects for expansion, and viability over time. Because SaaS companies mostly rely on recurring income from subscriptions, high churn rates can be detrimental to their operations. Customer retention is crucial for SaaS companies as it is frequently more profitable and cost-effective than bringing on new customers. Retention expenses and efforts can be decreased by focusing on an appropriate set of customers. This study focuses on an intelligent analytical framework that uses machine learning and artificial intelligence techniques to find the ideal group of customers for a SaaS-based organization to retain. The previous papers concentrated on either classification or survival analysis to determine the probabilities of churn. A few studies used explainable AI models to improve the predictability of the model predictions. Not having a holistic prediction model and retention strategies provides the research gap for this study. The proposed methodology used feature selection models to identify the most significant drivers of churn, and the most popular predictive models, like logistic regression, random forest, support vector machine, and neural networks, are applied to the training set. The likelihood of churn is calculated by using classification models. The Kaplan-Meier estimate is used for survival analysis to determine the odds of survival based on the tenure of each account. Lastly, the prediction models’ interpretability is enhanced by using explainable AI models like SHapley Additive exPlanations (SHAP) and Local interpretable model-agnostic explanations (LIME). The neural networks model gave the best accuracy of 71% for the classification model, which provided the probability of churn and the likelihood of survival, has been predicted by Survival Analysis. Explainable AI models have identified the most important features that the model considers when arriving at the probability. This enabled the company to segment the data based on the probabilities of churn and survival, and the feature importance and respective retention strategies have been planned for each segment. By implementing the suggested analytical methodology, the business may determine which customers are most important to target with customer retention strategies.