This study introduces a precise anomaly detection system for real-world Vehicle-to-Everything (V2X) mobility scenarios by employing the NS-3 network simulator to generate representative V2X mobility data while harnessing Deep Neural Networks (DNN) for accurate anomaly detection. This approach offers a dependable solution for identifying critical events in V2X mobility data, significantly enhancing transportation systems’ safety and reliability. The seamless integration of NS-3’s realistic simulations with Deep Neural Network highlights the potential of this approach for research purposes, granting flexibility and generating high-quality data. The positive impact of our model extends to traffic management and accident prevention, positioning it as a valuable tool in the evolving domain of intelligent transportation. With its ability to ensure the seamless integration and secure operation of Vehicle-to-Everything technologies, our approach plays a pivotal role in advancing the efficiency and safety of modern transportation systems.