Large supermarket run-centers, also known as Big Marts, now keep track of the sales volume and revenue figures for each individual product in order to estimate potential domestic demand and update inventory management. Inconsistencies and wide trends are frequently discovered by examining the data warehouse's database server. Businesses like Big Mart may use analytics to anticipate possible product sales using several machine learning techniques. To predict the sales of the products in the Big Mart, we used a variety of machine learning algorithms in this project, including Linear Regression, Ridge Regression, Lasso Regression, Decision Tree Regression, Random Forest Regression, Support Vector Regressor, Adaboost Regressor, and XGBoost Regression. We find that, of the methods described, XGBoost Regression performs the best in forecasting sales volume. In order to further increase the accuracy, we built a model using XGBoost Regression and fine-tuned it. This model is available on a flask application, where users may log in, enter the details of a product, and receive accurate forecasts of its sales