The systematic review under PRISMA methodology focuses on the interface of remote sensing (RS) with GIS and Artificial Intelligence (AI) in real-time, multi-hazard disaster management. Here, RS has a major role in hazard detection, GIS facilitates spatial analysis, AI predictive modelling, and resource optimization. Altogether, these tools propel data-oriented and interdisciplinary approaches to disasters like floods, earthquakes, fires, and maybe emblems of urban climate challenges as well. Deep learning and metaheuristic algorithms will further analyse big data sourced from RS and GIS to predict hazards and optimize response strategies. Sample case studies demonstrate the value of such integration in handling simultaneous disasters such as earthquake-induced landslides and urban flood risks from climate change. They examine risk zone identification, exposure, and vulnerability since these aspects inform decision-making, urbanization processes, and resource allocation. Nonetheless, several challenges continue to exist, including data quality, the non-transparency of AI models, and computational scalability. Future work would be intended to realize real-time data fusion and global-scale monitoring and thus change disaster management from reactive approaches to proactive ones. This should improve preparedness, resilience, and sustainability for different stakeholders among various geographies. Such a study hence alludes to how much RS-GIS-AI integration has to revolutionize disaster management for its flexibility and efficiency.