Artificial Intelligence (AI) has emerged as a transformative technology in the development of smart urban traffic monitoring and management systems. Rapid urbanization and increasing vehicle density have created significant challenges such as traffic congestion, road accidents, fuel wastage, and environmental pollution. Traditional traffic management approaches are often unable to respond effectively to dynamic traffic conditions. AI-based systems utilize technologies such as machine learning, deep learning, computer vision, neural networks, and the Internet of Things (IoT) to analyze real-time traffic data and optimize transportation operations. These intelligent systems can monitor traffic flow, detect incidents, predict congestion patterns, control traffic signals adaptively, and support autonomous transportation infrastructure. AI-driven traffic management enhances road safety, reduces travel time, minimizes fuel consumption, and improves urban mobility efficiency. Furthermore, smart traffic systems facilitate data-driven decision-making for city administrators and urban planners. Despite these advantages, challenges including high implementation costs, cybersecurity risks, data privacy concerns, and infrastructure limitations remain significant barriers. This study explores various AI techniques applied in smart urban traffic monitoring and management systems, reviews existing literature, examines research methodologies, and highlights the advantages and disadvantages of AI-based traffic solutions for sustainable and intelligent urban transportation development.
