Wireless Sensor Networks (WSNs) have become a widely researched subject in recent years due to their diverse applications. A key challenge in WSNs is managing energy consumption, as it directly impacts the network’s lifespan. Clustering has proven to be an efficient strategy for reducing energy use, where sensor nodes (SNs) are grouped into clusters, with each cluster having a designated cluster head (CH) to streamline communication and conserve energy. This paper presents an enhanced k-means clustering algorithm (IKCA), which reduces energy consumption by up to 35% and increases the number of alive SNs by up to 40% compared to existing methods. The algorithm employs swarm optimization techniques to determine near-optimal CHs. IKCA operates in two main phases: first, it selects the best CHs using the Puma Optimizer (PO), a modern swarm optimization algorithm, and second, it forms clusters using IKCA based on the centroids identified in the initial phase. The performance of IKCA is evaluated against four other clustering algorithms, with results showing that it significantly decreases energy usage while increasing residual energy in nodes. Furthermore, it maintains a higher number of active nodes compared to previous approaches, thereby extending the network’s operational duration.