Keeping current customers is frequently more cost-effective than obtaining new ones, customer churn prediction is an essential responsibility for firms in a variety of industries. The capacity of ensemble learning techniques to combine the capabilities of many base learners to increase predicted performance has made them more popular in recent years. This research work presents an overview of customer churn prediction in e-commerce utilizing ensemble learning techniques. Customer churn, the phenomenon of customers ceasing their relationship with a business, poses a significant challenge in the e-commerce sector. Ensemble learning methods offer a powerful approach to enhance predictive accuracy by combining multiple models. This work explores the application of ensemble methods with Random Forest, Gradient Boosting, and AdaBoost, in predicting customer churn within e-commerce platforms. It emphasizes the importance of feature selection and engineering to capture relevant signals indicative of churn behavior in online shopping contexts. This work outlines the steps involved in building ensemble learning-based churn prediction models, including data preprocessing, model selection, hyperparameter tuning, and performance evaluation. Challenges such as interpretability and scalability are discussed, along with potential avenues for future research to advance customer churn prediction in e-commerce. Overall, this work provides a succinct overview of leveraging ensemble learning for customer churn prediction in the dynamic landscape of e-commerce.