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The generation of forecasts is without a doubt one of the most significant areas in any sector's operations. The ability of forecasting algorithms to deliver an accurate result is hindered by the fact that consumer-focused products have unpredictable demand, an absence of sufficient historical data, and a very short life cycle. In this study, the case of Big Mart has been analyzed in order to forecast the sales of a variety of products and to get a better knowledge of the impact that a variety of circumstances have on the sales of those products. Using the statistical software platform known as “SPSS,” Proposed work carried out the statistical analysis on the dataset. CatBoost is used to construct a predictive model, and then that model is used to forecast sales. A high degree of accuracy was reached by the suggested model in comparison to previous machine learning techniques.