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Floods Are One Of The Most Frequent And Devastating Natural Disasters, Causing Significant Socio-economic And Environmental Damage. Kerala, A State In India, Has Witnessed Recurring Floods In Recent Years Due To Heavy Rainfall, River Overflow, And Climate Change Impacts. Accurate Flood Prediction And Detection Are Essential For Minimizing Losses And Supporting Effective Disaster Management. Traditional Flood Forecasting Models Often Fail To Capture The Complex, Nonlinear Interactions Among Hydrological, Meteorological, And Geographical Factors. To Address These Challenges, This Study Proposes A Flood Detection And Prediction System For Kerala Using Artificial Neural Networks (ANN) And Deep Learning Techniques. The System Leverages Historical Rainfall Data, River Water Levels, Soil Moisture, And Climate Variables To Train Predictive Models Capable Of Identifying Flood-prone Regions And Forecasting Potential Flood Events. By Integrating Data Preprocessing, Feature Engineering, And ANN-based Modeling, The Approach Enhances Prediction Accuracy And Reliability. The Results Demonstrate The Potential Of Deep Learning For Real-time Flood Monitoring And Early Warning, Thereby Supporting Proactive Disaster Risk Reduction And Resilient Planning For Kerala.