<p>Node localization is one of the most important problems in Wireless Sensor Networks (WSNs); it entails utilizing anchor nodes with known coordinates to estimate the locations of unknown nodes. Numerous bio-inspired methods have been put out thus far to achieve precise localization of these unidentified nodes. Furthermore, with the increasing proliferation of wireless sensor devices, there is increasing interest in location and tracking applications utilizing WSNs. It is still difficult to determine the best network settings for node localization during the network setup process in a timely manner while maintaining the required level of precision. Therefore, machine learning (ML) methods may be utilized to accurately forecast the Average Localization Error (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(ALE\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mi mathvariant="italic">ALE</mi> </mrow> </math></EquationSource> </InlineEquation>) based on the data. In this paper, the utilization of an Extra Gradient Boosting Regression (XGBR) ML model will be considered when combined with three optimizer models known as the Augmented Grey Wolf Optimizer (AGWO), Tunicate Swarm Algorithm (TSA), and Crystal Structure Algorithm (CSA) to accomplish improved performance. Results obtained after testing runs revealed that, indeed, among models tested, the best performance belonged to XGCS; its estimated R² value was very high, approximately 0.996. Thus, among all the models presented in this paper, the XGCS model can be regarded as the most successful. The results obtained are promising for professionals and people interested in the field, showing its possible applications and improvements in their work.</p>