Abstract: Severe traffic congestion remains a critical issue in many African urban centers, particularly Kampala, where outdated traffic management systems struggle to respond to real-time conditions. This study proposes an adaptive traffic decongestion model developed using Simulation of Urban Mobility (SUMO) software. The model utilizes real-time sensor data and intelligent algorithms to dynamically manage traffic flow at intersections, optimizing signal timings based on current conditions. Simulation results reveal a significant 54.2% reduction in vehicle waiting times compared to conventional traffic control methods. Beyond improving traffic flow, the system offers additional benefits, including reduced fuel consumption, lower greenhouse gas emissions, and increased economic productivity by minimizing congestion-induced delays. Designed with scalability and cost-efficiency in mind, the model is suitable for cities with limited infrastructure budgets. Its flexible architecture allows easy customization for various traffic patterns, intersection layouts, and infrastructural constraints. This solution presents a transformative, sustainable approach to urban traffic management, with the potential to reshape mobility in Kampala and other growing African cities, enhancing quality of life and reducing economic losses.