This paper presents TrafficIQ’s AI-powered smart signal system designed to reduce urban traffic congestion. The system predicts traffic volume using historical data and dynamically adjusts signal timing to optimize vehicle flow. It also monitors minor accidents and pedestrian movement to improve safety and reduce delays. The study reviews existing traffic management strategies, identifies gaps in infrastructure, policy, and technology adoption, and proposes a phased, data-driven implementation approach with stakeholder coordination. Findings show that smart signals enhance traffic efficiency, support emergency vehicle movement, and improve pedestrian safety. The paper demonstrates the potential of AI as a scalable, sustainable solution for intelligent urban mobility and effective congestion management.