In today’s competitive retail landscape, understanding consumer behaviour is very important. This study uses Market Basket Analysis (MBA), especially the Apriori algorithm, to scan transaction data and recognize product associations. It examines historical sales data and then reveals how promotions and discounts affects how consumers purchases products, which in return shows both sales and customer satisfaction. The study focuses on the practical applications of MBA, showing how it helps in optimizing where a certain a product should be placed, cross-selling and chosen advertising strategies. The Apriori algorithm is one of a kind due to its productivity and the ability to implement it easily, making it a valuable algorithm for introductory transaction data analysis. The findings of this research show the significance of a nuanced approach to promotions, also taking into account certain factors such as timing, categories and time span to balance short-term sales gains with long-term brand value. Overall, MBA, aided by structured algorithms like Apriori, provides retail businesses with functional understanding, enabling them to make well-briefed decisions, enhance marketing strategies, improve inventory management and achieve sustainable growth in an active market environment making sure that retailers can enhance the advantages of data-driven promotions and discounts, coinciding with consumer preferences.