High traffic flow management is a critical challenge in urban transportation systems, necessitating the development of efficient and effective strategies to alleviate congestion and enhance overall traffic performance. This review paper presents a comprehensive analysis of a traffic flow management system based on queuing theory, which has emerged as a promising approach to address the complexities of traffic dynamics and optimize traffic operations. Queuing theory provides a mathematical framework for modeling and analyzing the behavior of queues, making it well-suited for capturing the intricate interactions and dynamics within traffic networks. This paper discusses the fundamental principles and concepts of queuing theory and its application to traffic flow management. It highlights the key components of a queuing-based traffic management system, including data collection, queuing models, traffic control strategies, and performance evaluation techniques. Furthermore, the review paper examines the recent advancements and innovative approaches in utilizing queuing theory for traffic flow management. It explores various queuing models, such as single-server queues, multi-server queues, and network queues, and their applicability to different traffic scenarios. The paper also discusses the integration of queuing theory with advanced technologies, such as machine learning, data analytics, and intelligent transportation systems, to enhance the accuracy and efficiency of traffic management systems. Moreover, the review paper presents a comprehensive evaluation of the performance metrics used in assessing the effectiveness of queuing-based traffic management systems. It discusses measures such as queue length, waiting time, service time, throughput, and system capacity, and their implications for traffic flow optimization. The paper also addresses the challenges and limitations of queuing theory in traffic management and provides insights into potential areas of future research and development