Agriculture plays an important role in any nation’s economy. Agricultural marketing brings producers (farmers often in rural areas) and customers (customers often in rural areas) together at various location/distribution centres. Farm produce will be distributed to the location/centres based on the customers’ needs and hence requires that customers are segmented based on the needs and nearness to distribution centres. Segmenting customers and recommending other farm produce available at a distribution centre is based on multiple criteria including user demographics, and historical behaviour. This paper is aimed at segmenting the available customers and recommending available farm produce at various distribution centres for easy access for or by the rural farmers. The K-mean Algorithm is used for customer segmentation, while the association rule apriori algorithm for farm produce recommendation. The dataset of 300 farmers in different villages in Oruk Anam Local Government Area, Akwa Ibom State were collected, followed by the model implementation using python programming language in jupyter notebook and other web technologies. The Apriori algorithm was used to find the frequent item sets based on clusters from the RFM-K Means model. The frequent itemsets for different clusters (0,1,2) were derived and minimum support of 0.03 for cluster 2, 0.02 for cluster 1 and 0.01 for cluster 0 were applied to find the frequent itemsets (The algorithm grouped buyers and sellers into three clusters). Minimum threshold of 1, minimum confidence score of 0.7 and minimum lift score of =>4, 2, 2 for cluster 2, 1, 0 respectively were applied to the association rules to achieve a better result. The minimum support threshold determines the frequency required for a cluster to be considered relevant. Confidence in this represents the reliability of the association rules. A higher confidence threshold means more reliable recommendations. This work provides the platform for accessibility to rural farmers and the farm produce.