Flooding is one of the most destructive and harmful natural disasters occurring in many parts ofrnthe world and there is increasing evidence that losses are rising largely because more people arernsettling in flood prone areas. In many regions of the world, flood forecasting is one among thernfew feasible options to manage floods and Ethiopia has no exceptional. Flooding in the countryrnis mainly linked with heavy rainfall and the topography of the highland mountains and lowlandrnplains with river banks system formed by the major river basins such as Baro River. rnThis study presents Real Time Flood Forecasting system using Artificial Neural Network (ANN)rnand HEC-RAS integrated modeling in Baro River. ANN hydrological flood forecasting modelrnset up using both deterministic and stochastic approach with Rainfall, Temperature andrnTopographic Wetness Index (TWI) as parametric inputs and trained random neurons weights asrnstochastic variable. The hydrological model trained and validated using 7 years (1999-2005) andrnthree years (2006-2008) observed stream flow data respectively. And its performance alsornevaluated with 0.84 and 0.87 NSE values at calibration and validation period respectively.rnSimilarly, for hydraulics modeling, using Normal Difference water Index (NDWI) revealed thatrnboth recorded flood events and flood extent area obtained from HEC-RAS are overlapped up torn96% during calibration and validation. rnThe Real time forecasting of flood and its inundation area also evaluated using forecasted dailyrnrainfall and temperature for 3, 7 and 10-days during (May 27, 2019-June 05, 2019) rainy periodrnand these results further compared with the real time condition after 3, 7 and 10 days and showedrnvery good performance. In addition to these, three decades future flood affected areas withrndifferent climate change scenarios identified to warn the inhabitants and developmentrninvestments.