The huge supermarkets are more data-driven in todays retail world. These businesses tediously analyze sales data for each individual item they provide in order to optimize inventory management and predict managers demand. Using machine learning techniques, anomalies and patterns are being added to the data repository. This data is used to forecast future sales volume, which is critical for merchants like supermarkets. We provide a prediction model, similar to supermarkets, that uses the capabilities of the XGBoost algorithm to forecast a companys sales. Our findings show that our suggested model exceeds existing models in terms of predicted accuracy, illustrating the power of complicated machine learning approaches in optimizing retail operations. This study provides useful information for improving sales forecasting and inventory management.