Rapid urbanization and the exponential growth of vehicles have created significant challenges in traffic rule enforcement and management. Traditional traffic management systems often rely on manual monitoring and fixed-time signals, which are inefficient in handling dynamic traffic conditions. Artificial Intelligence (AI) has emerged as a transformative technology that enables real-time monitoring, predictive analysis, and automated enforcement of traffic rules. This research article examines the role of AI in effective traffic rules management through a case study of AI-enabled Intelligent Traffic Management Systems (ITMS). The study highlights the applications of AI such as automated number plate recognition, adaptive traffic signal control, violation detection, and predictive congestion analysis. The findings reveal that AI-based systems significantly reduce traffic violations, improve road safety, and enhance operational efficiency. However, challenges such as high implementation costs, data privacy concerns, and technical limitations remain critical barriers. The study concludes that AI-driven traffic management is an essential component of smart city development and sustainable urban mobility.