Accurate and rapid flood inundation mapping is critical for mitigating socio-economic and ecological disruptions, particularly in flood-prone regions like India. This study presents an advanced framework leveraging EOS-04 C-band SAR data, integrating multi-parameter analysis – including the normalized difference flood index (NDFI), temporal backscatter variability (standard deviation (SD)), and dual-polarization (HH/HV) metrics – to enhance detection accuracy in complex environments. Five machine learning models were rigorously evaluated across diverse hydrological basins, with random forest emerging as optimal (98.8% accuracy, 95.7% recall), processing flood layers in 85 s – a benchmark for rapid response. A user-friendly graphical user interface (GUI) was developed to operationalize near-real-time mapping, validated against optical satellite data (94% agreement), and deployed in high-risk zones such as the Godavari-Sabari, Ganga, and Brahmaputra basins. Key innovations include the first fusion of NDFI with temporal SD to distinguish dynamic flood signals from permanent water bodies. The framework's computational efficiency and open-source adaptability position it as a scalable tool for disaster agencies. By bridging gaps in real-time SAR analytics, this work advances flood resilience strategies, offering a template for adaptive risk management in monsoonal and climate-vulnerable regions globally.