Wireless sensor network (WSN) has become an emergent paradigm of networking and computing. It uses several application domains like military, healthcare, surveillance, target tracking, etc. Despite the benefits of WSN, node localization (NL) remains a crucial problem. It intends to define the exact place of unknown nodes from the network depends upon the anchor nodes. NL issues may be processed as non-deterministic polynomial-time (NP) hard problems and can be resolved using meta-heuristic optimization techniques. An enhanced seagull optimization-based node localization (ESGOBNL) scheme for WSN was developed in this aspect. The ESGOBNL approach aims to determine the proper location coordinates of the sensor nodes (SNs) in WSN. The proposed ESGBONL technique has been derived by the inclusion of the levy movement (LM) idea into the classification seagull optimization (SGO) algorithm, which is based on the migration as well as the attacking nature of seagulls. A wide range of experiments was implemented in order to report the promising localization efficacy of the ESGOBNL approach. The extensive comparative outcomes highlighted the betterment of the ESGOBNL system on the recent approaches with minimal mean localized error (MLE) of 0.120214. In contrast, the elephant herding optimization (EHO), hybrid elephant herding optimization (HEHO), and tree growth algorithm (TGA) techniques have obtained maximum MLE of 0.793293, 0.333199, and 0.280031, respectively.