Sales forecasting is a crucial aspect of business strategy, aiming to predict future demand and optimize inventory management and marketing efforts accordingly. By accurately anticipating sales trends, companies can avoid overstocking and unnecessary costs while making informed decisions across various operational domains like sales, production, purchasing, finance, and accounting. In this paper, we delve into both internal and external factors that directly impact consumer sales demand in supermarkets. These factors include temperature, fuel prices, holidays, Consumer Price Index, Employment rate, and discount strategies. Drawing upon existing research and expert insights, we identify suitable methodologies and machine learning algorithms to analyze and predict sales patterns effectively.