Wireless Sensor Networks (WSNs) are pivotal for various applications, but they face significant challenges, primarily due to the limited battery life of sensor nodes. The clustering of sensor nodes is a key research domain within WSNs, aimed at improving energy efficiency by organizing nodes into clusters and optimizing Cluster Head (CH) selection. Efficient CH selection is crucial for balancing the network load and extending its lifespan. Hence, in this paper, the challenge of CH selection is addressed by proposing an enhanced method that leverages the Grey Wolf Optimization (GWO) algorithm, incorporating node centrality into the fitness function. Additionally, the data transmission is optimized using the Threshold-sensitive Energy Efficient Network (TEEN) protocol to minimize data redundancy and improve overall energy efficiency. The proposed GWO-TEEN approach achieves a mean energy consumption of 27.58, outperforming other methods such as I-GWO (78.22), FI-GWO (148.74), and F-PSO (191.41), demonstrating its effectiveness in enhancing energy efficiency and network longevity.