Road traffic violations remain a major challenge worldwide, contributing to accidents, injuries, and fatalities. Most existing traffic enforcement methods often rely on manual monitoring and static rule-based systems, which are inefficient in identifying repeat offenders and predicting future violations. This research proposes the design and implementation of a hybrid system for profiling and predicting traffic offenders using deep learning algorithms, aimed at enhancing law enforcement strategies and improving road safety. The system integrates unsupervised learning (K-Means clustering) for categorizing offenders into high-risk, medium-risk, and low-risk groups based on historical violation patterns, and supervised deep learning models (LSTMs) for predicting future offenses. By leveraging large-scale traffic data, the system enables proactive intervention by law enforcement agencies. The implementation utilizes Python, TensorFlow, and Scikit-learn libraries, with cloud-based infrastructure for real-time data processing and scalability. Performance evaluation using real-world traffic datasets demonstrates the system’s effectiveness, with high accuracy in offender classification and future offense prediction. Compared to conventional enforcement techniques, the proposed AI-based hybrid approach enhances traffic monitoring, risk assessment, and predictive policing. This research contributes to advancing intelligent transportation systems, AI-driven law enforcement, and smart city initiatives, providing a scalable and automated framework for improving road safety.