A well-organized approach for misbehavior detection in VANETs by uniting the Gray Wolf Optimizer (GWO) and Random Forest (RF) algorithm was studied. VANETs display security risks due to the constant changing nature of vehicles, making misbehavior detection an important assignment. The GWO algorithm is used to evaluate the finest features that have a remarkable impact on detecting misbehavior in VANETs. Feature selection reduces the dimensionality of dataset and improves the misbehavior detection system’s overall productiveness. The chosen features are then put into RF classifier, which uses an ensemble learning method to differentiate normal and unusual vehicular behavior accurately. The RF algorithm is used to increase the high dimensionality of VANET data efficiently, gives high accuracy, and reduces computational difficulty. For the experimental purpose, VeReMi is utilized to evaluate and to show how well it can be used to detect misbehavior in VANETs. A hybrid combination of GWO and RF improves detection accuracy compared to individual algorithms. The proposed approach has implications in enhancing the security of VANETs by identifying anomalous behavior and ensuring the safety and efficiency of vehicular communications.