The proposed paper focuses on the basic concepts of association rule mining and the market basket analysis of different items. In the current study, the market analysis would be done by collecting the real, primary data directly from retailers and wholesalers. The efficiency of the FP- Growth algorithm can be measured in terms of mining of the frequent pattern. Precisely, we apply the FP-Growth algorithm on the various data collected from different stores in order to trace the various association rules comprising of a basket. One discrete advantage is that it avoids the generation of candidate sets, which is computationally exhaustive. The results and conclusions drawn can be used in optimizing the market. This will help in predicting future trends and behaviors, allowing businesses to make knowledge-driven decisions.