E-Commerce product returns are considered as a major disease and is also a very challenging issue that greatly impacts the revenue of the E-Commerce firm. Most of the E-Commerce firms consider 10% of the return rates to be normal, but in cases when the product return rate exceeds 10%, the investigation to such cases are further needed. The rising of the return rate is considered as a big threat to the e-commerce industry, which needed to be slowed down. Predictions can be carried out to overcome the return issues in advance and measures can be taken to decrease the return rate. In this paper, the work focuses on developing a prototype model for predicting the return rate of any particular product in advance. In the existing system the return volume management is predicted based on the dependent variable of the manufacturer’s production process and their resources alone. Our work focuses on finding the return rate by including a few more parameters which in turn enhances the prediction accuracy. The proposed work is tested on different Machine Learning algorithms for optimizing the results. The results can be applied to overcome the major return and loss percentage by the e-commerce industry to enhance their future revenue.