Increasing congestion and complexity in urban transportation networks have reduced the effectiveness of static control strategies, highlighting the need for data-driven smart traffic management to mitigate delays, improve safety, and reduce environmental impacts. This paper adopts a review-and-design perspective to articulate a conceptual Smart Traffic Management System (STMS) that aggregates and fuses real-time, multi-source data (e.g., roadside sensors, cameras, and connectivity-based signals). The framework leverages computer vision to extract traffic states and detect incidents, and employs machine-learning analytics for forecasting and decision support, enabling adaptive signal control, incident management, and emergency-vehicle prioritization through an end-to-end pipeline from data ingestion and preprocessing to feature extraction, inference, and control action. The main contribution is a structured STMS architecture and operational workflow that clarifies the required components and decision pathways, and qualitatively demonstrates the system’s potential to outperform static timing by reducing queue build-up and waiting time, stabilizing flow, and improving responsiveness under non-recurrent events. The study is primarily conceptual and does not report quantitative field or simulation-based results; standard ITS performance indicators (e.g., average delay, travel time, queue length, throughput, emissions) and detection metrics (e.g., precision/recall/F1) are not empirically evaluated. Practical effectiveness is also contingent on data quality, sensor coverage, integration with legacy controllers, and security/privacy constraints. Integrating real-time sensing, computer vision, and machine-learning–driven decision support provides a viable foundation for smart-city traffic operations. Future work should prioritize pilot deployments, rigorous quantitative evaluation using established ITS metrics, and robust designs that address noisy data, operational constraints, and governance requirements.