City flood control is a significant concern everywhere due to the constantly changing environment. The modern world needs smart cities with smart infrastructure to manage or control floodwaters. The research objective of this study is to design real time urban flood control methodology, develop the working model and testing the model with result analysis in controlled environment. This research paper proposes a smart water control model based on fuzzy inference system. The research is advancement in the Water Sensitive Storm Water Management System by creating a prototype model and then evaluating it in real-world scenarios using input parameters as rainfall intensity, water flow rate, and water level. The method relies on water catchment flooding data that was collected in real-time using sensors and an autonomous smart controller. The system considers the real-time sensor data from all catchments to make collective decision, which also optimize the use of actuators by conserving the power used by the actuators. In terms of early floodwater control, the recommended approach optimizes the use of actuators with utilizing the existing drainage system. The average water reduction rate at the medium level is 34.8%. At high levels, the average water reduction rate is 61.43%, and at extremely high levels, it 73.63%. A significant reduction of water level achieved in the most inundated area by 73.9 % in high and extreme input parameter value
