Systems known as network intrusion detection systems, or NIDS, keep an eye out for any unusual activity that can jeopardize the confidentiality, availability, or integrity of the network's resources. It is critical to recognize the vulnerabilities in NIDSs and make sure they fulfill their essential functions by taking an antagonistic stance and spotting weak points and security holes. As a result, strong defenses against any prospective attacks may be created. The proposed methodology ensures constant interconnectivity among the dynamic vehicle nodes by integrating block chain concepts with a special time stamping mechanism. Furthermore, the architecture verifies the accuracy of the events recorded in the RSU by utilizing block chain concepts. It also proposes a flexible hybrid trust model that uses the latest direct and proposed trust evaluation approaches to compute trust between the vehicular entities. The system also includes a threading mechanism to schedule message execution in direct trust evaluation and clustering algorithms relevant messages in indirect trust assessment. To evaluate the efficacy suggested, numerous experiments are carried out in a simulated setting, including comparisons with other pertinent research. The findings show that the proposed trust model has a 92% rate in identifying malicious nodes. The integration of artificial intelligence (AI) and vehicle-to-everything (V2X) technology enhances driving efficiency, comfort, and safety by extending the driver’s field of view, gathering data from multiple sources, and predicting potential collisions. This offers a summary initiatives that use artificial intelligence to address issues in V2X systems.