Urban flooding poses a growing threat to rapidly expanding cities in the Global South, with Agartala, the capital of Tripura in Northeast India, experiencing recurrent inundation due to its low-lying topography, intense monsoon rainfall, and deteriorating drainage infrastructure. This paper provides a systematic review of satellite remote sensing and artificial intelligence (AI) models for urban flood mitigation within the context of Agartala Smart City. The study synthesizes findings from peer-reviewed literature, government reports, and remote sensing analyses conducted between 2015 and 2025. Our analysis reveals that approximately 9.2% of Agartala's total geographical area falls within high to very high flood risk zones, with seven out of 35 wards identified as critically vulnerable. Sentinel-1 Synthetic Aperture Radar (SAR) imagery from flood events demonstrates that 374.5 hectares were inundated during a single extreme rainfall event in August 2024. AI-based flood susceptibility models, including Random Forest (RF), Support Vector Machines (SVM), and Convolutional Neural Networks (CNN), achieve classification accuracies of 85-92% for urban flood mapping. Emerging Geo-Foundational Models (GFMs) show promise for transfer learning applications where labeled training data remains scarce. The paper proposes an integrated framework combining real-time SAR monitoring, AI-based predictive modelling, and smart city infrastructure (sensors, command centers, citizen reporting) to enhance flood resilience. Key policy recommendations include updating drainage master plans based on LiDAR-derived Digital Elevation Models (DEMs), deploying IoT-based water level sensors across 50 identified hotspots, and establishing a public-facing flood early warning dashboard. This review contributes to the growing literature on climate-adaptive smart cities in data-sparse environments.

Keywords: Urban flood mitigation, Synthetic Aperture Radar (SAR), artificial intelligence, Agartala Smart City, flood susceptibility mapping, resilience planning, geospatial analysis