Urban flash floods present significant challenges, especially in arid regions like Al-Ain City, UAE, where rapid urbanization and climatic extremes exacerbate vulnerabilities. This study addresses the critical need for an accurate flood vulnerability assessment by integrating Geographic Information Systems (GIS), Remote Sensing (RS), the Analytical Hierarchy Process (AHP), and Multi-Criteria Decision Analysis (MCDA). By systematically evaluating key physical and social factors-such as population density, impervious surfaces, elevation, and rainfall intensity-the research identifies high-risk flood-prone areas, particularly in central districts like Al-Jimi and AlMuwaiji. GIS spatial modeling, supported by remote sensing data, enabled the generation of a detailed vulnerability map, categorizing zones into low, medium, and high-risk categories. The findings reveal that dense urbanization, low elevation, and inadequate drainage infrastructure significantly increase vulnerability. Conversely, areas with higher elevations and natural vegetation, like Jebel Hafeet, exhibit resilience. The methodology's robustness lies in its integration of diverse data sources, weighted overlay analysis, and pairwise comparisons, ensuring precision in identifying and prioritizing mitigation strategies. This research not only provides actionable insights for urban planning and disaster risk management in Al-Ain but also underscores the potential of combining GIS, RS, AHP, and MCDA in environmental decision-making to foster climate resilience and sustainable urban development.