In the era of data-driven decision-making, mining valuable patterns from large datasets is imperative. We have explored association rule mining techniques to find hidden patterns. The main objective of this paper is to identify meaningful associations among the items in the dataset. We have implemented both the Apriori algorithm and the FP-growth algorithm to discover the hidden pattern. By employing association rule mining techniques, the research aims to provide actionable insights into consumer behavior and preferences, facilitating informed decision-making for retailers. We considered the minimum support and minimum confidence to be 20% extracts of the significant relationships among the items. The study utilizes metrics such as lift, leverage, and confidence to assess the relevance and reliability of the discovered associations. This research paper describes several frequent item sets with a minimum support of 20%, indicating strong patterns. From the obtained rules, associations include the co-occurrence of “Cheese” and “Bread” with a confidence of 46.15% and “Eggs” and “Bread” with a confidence of 60%. Strong association rules were derived from both algorithms, showcasing relationships like “Cheese” and “Bread,” and “Eggs” and “Bread” with substantial support and confidence. We have compared the two different algorithms in terms of their execution speed. Then we applied a hybrid approach of both Apriori and FP Growth and executed it on the dataset. It is observed that the hybrid approach gives a better performance as compared to the other two algorithms. So, it is concluded that the hybrid of Apriori and FP Growth algorithms outperforms well.