Increasing competition in the retail industry requires businesses to understand customer purchasing patterns to support more effective decision-making. This study applies Market Basket Analysis (MBA) using the Apriori algorithm to identify product associations and generate data-driven marketing recommendations. The research follows the CRISP-DM methodology, consisting of business understanding, data understanding, data preparation, modeling, evaluation, and deployment stages. The dataset was obtained from the Retail POS Basket Data on Kaggle, comprising 1,991 unique transactions and 68 product categories. The analysis results indicate that a minimum support of 0.2% and a minimum lift of 1.0 produced 2,370 frequent itemsets and 1,974 association rules. The strongest rule identified was {Energy Drink, Fish} -> {Potato}, with a confidence value of 66.7% and a lift value of 9.69. Furthermore, the resulting association rules were implemented in an interactive Streamlit-based dashboard. The findings demonstrate that Market Basket Analysis can provide valuable insights to support marketing strategies and improve sales performance in the retail sector.