<p>Floods are considered one of the most damaging forms of natural disaster and require accurate forecasting to enhance disaster management. Although conventional methods of flood forecasting have provided valid information, they are often inadequate for manning and large-scale purposes due to data limitation and necessary weather condition dependency. Although progress in geospatial technology has advanced considerably, there continues to be a paucity of work which appraises the interrelated effects of remote sensing and A.I. on flood forecasting, especially in making adequate provision for the conventional impediments and employing the new technologies. Consequently this review will endeavor to examine how remote sensing and A.I. have revolutionized flood forecasting, especially in terms of progress and present problems and future opportunities to advance flood monitoring and disaster resiliency of individuals. The study is based on scrutinizing different types of remote sensing techniques e.g. optical, microwave and thermal sensors, together with A.I. techniques e.g. machine learning and deep learning. In the literature study an appraisal of world wide satellites and their differing type of computational methods in flood researching highlighted the state of technologies of current state of the art and their integration for flood management. The current analysis is founded on the observation that the integration of remote sensing together with A.I. facilitates the dynamic and automatic flood monitoring. Methods of flood monitoring through SAR based microwave sensing coupled with LiDAR and multispectral imaging have vastly improved real time mapping for floods, even in conditions of adverse weather conditions. It is apparent that the A.I. models of neural network and ensemble learning which are quite complex in as analysing the data base supplied with accuracy improved flood forecasting techniques. Nevertheless problems still exist e.g. of integrating data, computational requirements and the procurement of high resolution imagery of data base without cost problems. Future investigations should lead towards combining the newer technologies e.g. quantum sensors, IOT based monitoring and digital twins, in this way leading to greater efficiency in enhanced forecasting system. This review will also seek to provide a better understanding of modern technological problems, available markets and future opportunities at the intersection of RS and A.I. and associated flood resiliency systems.</p>