Predicting sales and demand is a crucial responsibility in the retail sector because it enables businesses to make educated choices about their inventory policies, pricing plans, and advertising activities. The effectiveness of machine learning algorithms in predicting sales has been demonstrated, and two well-liked methods are random forests and linear regression, respectively. An ensemble learning technique called random forest mixes various decision trees to produce predictions that are more precise. On the other hand, linear regression is a straightforward yet effective technique that simulates the relationship between factors and sales. In this paper, we compare the performance of random forest and linear regression in predicting sales using the Big Mart sales dataset. This dataset contains information on sales for various products in different stores, along with variables such as product weight, store location, and promotional offers. We evaluate the performance of both algorithms in terms of accuracy, speed, and interpretability and discuss the practical implications of the findings. This study can help businesses choose the appropriate algorithm for their sales prediction needs and provide insights into the strengths and limitations of each algorithm.