This chapter presents a systematic review of recent advancements in intelligent strategies for urban traffic flow modeling, with a focus on the integration of IoT-enabled sensing, distributed computing, and artificial intelligence. Through a rigorous screening protocol, 164 high-quality studies published between 2020 and 2024 were analyzed. The results reveal two main technological pathways: (i) enabling infrastructures such as adaptive traffic signals, V2X communications, and distributed edge–fog–cloud computing, which collectively reduce delays, increase throughput, and enhance road safety; and (ii) advanced data analytics, where machine learning, deep learning, and graph-based models significantly improve the accuracy of congestion forecasting and anomaly detection. Empirical findings report reductions of up to 30% in traffic delays, 50% in intersection congestion, and 28% in CO2 emissions when AI and IoT are jointly deployed. Despite challenges in scalability and interoperability, the evidence demonstrates that data-driven and hybrid approaches offer substantial potential for transforming traditional traffic management into adaptive, real-time intelligent systems. These insights underscore the strategic importance of emerging technologies in achieving sustainable and resilient urban mobility.