Ensuring customer loyalty has become a significant challenge across all sectors today. In highly competitive industries, accurately analyzing customer behavior and developing targeted marketing strategies are crucial. One effective approach for restaurants is to design personalized campaigns that enhance customer retention. In this study, real customer data collected from various application channels serving takeaway food customers were analyzed. The data clustered using the RFM (Recency, Frequency, Monetary) method combined with k-means clustering, a machine learning technique. The proposed methodology integrates the RFM model with machine learning to automatically cluster customers and segment each cluster, enabling the development of tailored marketing strategies.