Traffic congestion has become a major challenge in urban areas, leading to increased travel time, fuel consumption, and pollution. The advancement of the Internet of Things (IoT) has enabled real-time traffic monitoring and management, reducing congestion effectively. This paper presents an IoT-based system that leverages sensors, cloud computing, and machine learning to monitor and analyze traffic conditions dynamically. The proposed system offers an efficient solution for traffic authorities and commuters by providing real-time congestion updates, alternate route suggestions, and emergency response facilitation. The implementation of this system enhances urban mobility and contributes to sustainable transportation management. Additionally, this study explores the integration of intelligent transportation systems (ITS) with IoT-based frameworks to improve efficiency and scalability in traffic control. Keywords: IoT, Traffic Congestion, Real-Time Monitoring, Traffic Management, Smart Cities, Cloud Computing, Machine Learning, Intelligent Transportation Systems.