Node localization is a non-deterministic polynomial time (NP-hard) problem in Wireless Sensor Networks (WSN). It involves determining the geographical position of each node in the network. For many applications in WSNs, such as environmental monitoring, security monitoring, health monitoring, and agriculture, precise location of nodes is crucial. As a result of this study, we propose a novel and efficient way to solve this problem without any regard to the environment, as well as without predetermined conditions. This proposed method is based on new proposed Nutcracker Optimization Algorithm (NOA). By utilizing this algorithm, it is possible to maximize coverage rates, decrease node numbers, and maintain connectivity. Several algorithms were used in this study, such as Grey Wolf Optimization (GWO), Kepler Optimization Algorithms (KOA), Harris Hawks Optimizer (HHO), Gradient-Based Optimizer (GBO) and Gazelle Optimization Algorithm (GOA). The node localization was first tested in Istanbul, Turkey, where it was determined to be a suitable study area. As a result of the metaheuristic-based approach and distributed architecture, the study is scalable to large-scale networks. Among these metaheuristic algorithms, NOA, KOA, and GWO have achieved significant performance in terms of coverage rates (CR), achieving coverage rates of 96.15%, 87.76%, and 93.49%, respectively. In terms of their ability to solve sensor node localization problems, these algorithms have proven to be effective.