In response to key challenges in urban traffic management, especially in ensuring compliance with traffic regulations, this paper proposes an Packet Grey Wolf Optimization (PGWO) algorithm designed to improve prediction accuracy and enforcement efficiency in traffic violations. By introducing a momentum coefficient, a grouping position update strategy, and a reverse learning mechanism, the PGWO algorithm significantly improves global search capability and convergence speed, effectively avoiding early convergence to a local optimum. Taking the accurate identification of traffic violations as the core issue, this study applies PGWO algorithm to the traffic violation prediction model based on the Stanford Open Policing Project dataset. By comparing and analyzing the original Grey Wolf Optimization algorithm and other traditional optimization algorithms, PGWO showed excellent performance in improving the accuracy of traffic violation arrest. In addition, the PGWO algorithm has been integrated into the PNN regression prediction model, and its effectiveness and superiority in the field of traffic laws have been further verified through testing on the Kaggle dataset. The experimental results demonstrate that the PGWO algorithm not only achieves greater accuracy in predicting traffic violations but also enhances the model’s generalization ability, providing a new optimization strategy and decision support tool for intelligent traffic management and regulatory compliance.