Identifying flood risk-prone areas in the regions of extreme aridity conditions is essential for mitigating flood risk and rainwater harvesting. Accordingly, the present work is addressed to the assessment of the flood risk depending on spatial analytic hierarchy process of the integration between both Remote Sensing Techniques (RST) and Geographic Information Systems (GIS). This integration results in enhancing the analysis with the savings of time and efforts. There are several remote sensing-based data used in conducting this research, including a digital elevation model with an accuracy of 30 m, spatial soil and geologic maps, historical daily rainfall records, and data on rainwater drainage systems. Five return periods (REPs) (2, 5, 10, 25, 50, 100, and 200 years) corresponding to flood hazards and vulnerability developments maps were applied via the weighted overlay technique. Although the results indicate lower rates of annual rainfall (53–71 mm from the southeast to the northwest), the city has been exposed to destructive flash floods. The flood risk categories for a 100-year REP were very high, high, medium, low, and very low with 17%, 41%, 33%, 8%, and 1% of total area, respectively. These classes correspond to residential zones and principal roads, which lead to catastrophic flash floods. These floods have caused socioeconomic losses, soil erosion, infrastructure damage, land degradation, vegetation loss, and submergence of cities, as well life loss. The results prove the GIS and RST effectiveness in mitigating flood risks and in helping decision makers in flood risk mitigation and rainwater harvesting.