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Wireless Sensor Networks (WSNs) are gaining prominence for diverse applications, including environmental monitoring and industrial automation. Yet, their energy constraint poses a significant challenge. Clustering, a prevalent technique, optimizes energy utilization by grouping nodes into clusters and appointing a cluster head (CH) to aggregate data and communicate with the base station (BS). This paper presents a novel clustering and CH selection algorithm for a energy varied WSNs, leveraging modified fuzzy c-means (FCM) clustering and Grey Wolf Optimization (GWO). Modified FCM partitions nodes based on their similarity, while GWO identifies CHs in each cluster, considering energy levels, centrality, distance from the BS, and dynamic node distribution. Simulation results demonstrate the superior energy efficiency and network lifetime of our proposed approach compared to existing algorithms.