Localization in WSNs is very much significant part for variety type of applications such as military operation, intelligent transportation and environmental monitoring. Conventional localization techniques have so many disadvantages such as high computing overhead, restricted scalability, high energy consumption, low accuracy in harsh environments, vulnerability to noise and interference, lack of robustness, etc. To overcome these limitations, metaheuristic methods are being developed to provide more efficient, accurate and robust localization process. These limitations are particularly prevalent for methods that rely heavily on range-based measurements or global positioning system (GPS) which are expensive and typically impractical for large-scale implementations. In this research, a metaheuristic method called Hybridized Greylag Goose Red Kite Optimization (HGGRKO) is proposed to address these issues. This newly developed HGGRKO strategy combines two bio-inspired techniques known as Red Kite Optimization (RKO) for exploitation and Greylag Goose Optimization (GGO) for exploration. By integrating these two techniques, HGGRKO successfully achieves a balance between global search and local refinement to reduce localization error and improves anchor node selection as well. When simulated in MATLAB, the HGGRKO approach produces superior results than traditional techniques like Particle Swarm Optimization, Seige Whale Optimization Algorithm (SWOA) and Gray Wolf Optimization (GWO). The proposed method computes more quickly, requires fewer resources and as lower localization error than the conventional methods. However, HGGRKO retains excellent accuracy and significant scalability in dense and large-scale networks. By improving efficacy and accuracy, the proposed approach establishes a new benchmark for WSN localization. In order to address the current challenges, this study proposes the HGGRKO method which advances wireless sensor technology. The HGGRKO method will also enable future research on energy efficient routing, realistic deployments in difficult situations and 3D localization.