[Objective] Traditional monitoring methods for flood disasters primarily rely on single ground-based stations，which often suffer from limited spatial coverage，vulnerability to extreme weather，and delayed response capabilities. This research aims to address these limitations by systematically reviewing the “Space-Air-Ground” integrated monitoring framework. The primary objective is to investigate the synergistic mechanisms and key emerging technologies—such as Multi-source Data Fusion，Geospatial Artificial Intelligence （GeoAI），and Digital Twins—that enable high-precision，real-time，and intelligent flood perception and decision support. [Methods] The study adopts a multi-dimensional perspective to analyze the integrated monitoring architecture consisting of three layers： Space （Satellite clusters），Air （UAVs and aviation platforms），and Ground （IoT sensors and hydrological stations）. It evaluates the synergy between these layers across the Data，Transmission，and Application tiers. Specifically，the research reviews three levels of data fusion： data-level，feature-level （utilizing Cross-modal Transformers），and decision-level. Furthermore，it explores advanced data assimilation techniques，comparing traditional variational and sequential methods with modern Deep Learning-based approaches like PINN （Physics-Informed Neural Networks） and End-to-End Neural Assimilation. The study also classifies GeoAI applications in flood monitoring into four categories： inundation extraction，water level inversion，discharge estimation，and vulnerability assessment. [Results] The integrated framework significantly enhances monitoring reliability by achieving multi-layered spatio-temporal complementarity. Key results include：（1） Synergistic Perception： Ground-based sensors provide high-confidence “truth” data to calibrate satellite and UAV inversions，while satellite signals can trigger UAV swarms for targeted detailed inspections in a “discovery-tracking-verification” loop. （2） GeoAI Advancement： Modern models like Segment Anything Model （SAM） and Bitemporal Image Transformer （BIT） have improved water body segmentation accuracy under complex conditions，such as urban shadows or cloud cover，by utilizing cross-modal feature reconstruction. （3） Digital Twin Evolution： The technology has evolved from geometric visualization （L1） and physical mechanism simulation （L2） to logic-driven intelligent prediction （L3）. The integration of Reduced-Order Models （ROM） and GeoAI allows for “second-level” decision feedback by shifting computational burdens to the pre-training phase. （4） Operational Efficacy： Pilot applications in the Changjiang and Pearl River basins demonstrate that digital twin systems can support the “Four Pre-s” （Forecasting，Warning，Rehearsal，and Planning），providing critical technical support during major flood events. [Conclusions] This research concludes that the “Space-Air-Ground” integrated network represents the future of intelligent flood disaster management. The study’s innovation lies in proposing a vertically integrated “Perception-Model-Intelligence” architecture that moves beyond simple multi-source observation to a hybrid synergistic mechanism driven by both physical laws and data. However，several challenges remain：（1） technical barriers in high-precision cross-modal spatio-temporal alignment；（2） the lack of physical interpretability in “black-box” GeoAI models； and （3） the need for deeper coupling between digital twin systems and actual flood control business workflows. Future research should focus on：（1） Edge Intelligence： Developing lightweight algorithms for on-orbit real-time data processing. （2） Physics-Driven Sensing： Embedding hydrological mechanisms into neural networks to ensure consistency with physical laws. （3） Standardization： Establishing unified technical standards and interface specifications for digital twin flood systems to enhance their engineering applicability and cross-regional generalization.