Floods are among the most destructive natural disasters threatening lives, communities, and economies, with annual damage reaching billions of pounds worldwide. Human activities and recent climate emergencies are exacerbating the frequency and severity of these catastrophic events, exposing large communities to the risk of such natural hazards. Various mitigation techniques at both community and property levels can be adopted to provide responsive solutions for natural threats. In this study, a transformative steel modular housing system capable of rising above the ground in the event of a flood is presented. The robustness and efficiency of such an innovative system were tested on a full-scale prototype at the state-of-the-art HR Wallingford testing facility for flood resilience in the UK. To extend the applicability and assess the reliability of the innovative flood-resilient system, a comprehensive numerical investigation was carried out to check whether the modular system can reliably withstand multiple natural hazards, such as flooding, strong winds, and seismic ground motions. A refined numerical model was first calibrated on the basis of experimental outcomes to create a digital twin of the tested building. Such a model was then used to demonstrate the effectiveness of the proposed modular steel building under different flooding scenarios, i.e., in configurations with increasing height above the ground, namely 300 mm, 600 mm, and 900 mm. The results of experimental tests and the comprehensive parametric numerical analyses demonstrate that the proposed newly developed steel modular housing system ensures structural integrity, adequate performance, and resilience even for extreme flood scenarios characterised by rapid water velocities and severe wind conditions. The innovative and resilient modular housing system presented has also been demonstrated to be reliable for areas with moderate seismicity, i.e., with peak ground accelerations lower than 0.25 g. The proposed resilient and sustainable adaptation technology can thus be employed efficiently in regions worldwide that are exposed to multiple natural hazards, e.g., floods, high winds, and earthquakes.