Data clustering is a well-known data analysis methodology in the optimization community that can be formulated as a Np-hard problem. The main concept of the clustering procedure is to identify and assign data objects into sensible disjointed groups based on similarity measures, such as the Euclidean distance. In this case, data objects within a group are similar to each other with regard to the similarity measure, and each group is dissimilar to each other. Fuzzy clustering is also a popular and powerful research method with many applications, such as medical imaging processing with real words. In the fuzzy clustering research field, the fuzzy c-means (FCM) algorithm is one of the most important fuzzy clustering approaches for efficient and easy implementation; however, the FCM algorithm has a weakness in which it can easily become trapped in local optima. Thus, some metaheuristic algorithms, such as genetic algorithm (GA) and particle swarm optimization (PSO), can be combined with FCM to solve the clustering problem. Recently, a novel metaheuristic algorithm inspired by the social behavior of humpback whales, called the whale optimization algorithm (WOA), has been proposed. In addition, the WOA has obtained more efficient solutions than some popular metaheuristic algorithms, such as the gene GA and PSO. In this paper, a fuzzy clustering algorithm that called the memetic fuzzy whale optimization (MFWO) algorithm, is proposed to solve the data clustering problem. The performance of the proposed algorithm is evaluated using eight real-world UCI benchmarks, and the FCM and fuzzy PSO algorithms are compared. The experimental results show that the proposed MFWO algorithm is more efficient than the compared algorithms.