Abstract: This paper focuses on the implementation of Soft-Computing Technique (Artificial Neural Network) on Flood Management System. This paper presents an alternate approach that uses artificial neural network to simulate the critical level dynamics in heavy rain. The algorithm was developed in a decision support system environment in order to enable users to process the data. The decision support system is found to be useful due to its interactive nature, flexibility in approach and evolving graphical feature and can be adopted for any similar situation to predict the critical level. The potential benefit of a flash flood forecast depends on three main factors. Firstly its accuracy, which in turn depends on the accuracy of the forecast data, the observational data and the numerical weather modeling and updating procedures. Secondly the magnitude of the lead time it provides before critical levels are reached which can be improved by using quantitative precipitation forecasts from meteorological satellite cloud image, weather radar and numerical weather prediction models. Thirdly, the benefits depend on the effective use of the forecast information, for flood monitoring, flood warning, the operation of flood protection structures and the evacuation of people and livestock. This requires appropriate decision information in a timely manner to those who need it, where they need it, in a manner that is easy to understand. Finally, use of Artificial neural network may serve as a tool for real-time flood monitoring and process control.