Wireless Sensor Networks (WSNs) are increasingly used in wildfire detection, military surveillance, and smart agriculture. A critical challenge in these networks is accurate node localizationNode localization, where the locations of unknown nodes are estimated using the reference positions of anchor nodes. Localization accuracy directly influences the performance and reliability of WSNs, particularly in dynamic or harsh environments. Traditional localization techniques often suffer from high computational complexity and poor scalability. This paper introduces the Centroid-Based Whale Optimization AlgorithmWhale optimization algorithm (CB-WOA) to enhance localization in WSNs. The algorithm incorporates three key strategies, which include centroid-based initialization to enhance population diversity, nonlinear sinusoidal modulation for adaptive exploration and exploitation balance, and a scaled spiral update mechanism to strengthen global search capability. Performance evaluationPerformance evaluation under random deployments demonstrates that CB-WOA consistently outperforms existing algorithms such as JAYA, BOA, and PSO. Compared to conventional approaches, CB-WOA reduces the mean localization errorMean localization error by 70–82%, increases the proportion of nodes successfully localized by 11–38%, and lowers computational overhead, offering an efficient, scalable solution for resource-limited WSNs.