<![CDATA[Flood disasters generate substantial socio-economic and environmental impacts, increasing the need for accurate Flood Susceptibility Mapping (FSM). This study provides a comprehensive bibliometric analysis of FSM integrated with Multi-Criteria Decision Making (MCDM) and remote sensing techniques. Using the PRISMA framework, a total of 1,234 scientific articles were collected from the Scopus database and analyzed using VOSviewer to identify research trends, collaboration networks, and thematic clusters. The findings indicate a significant increase in publications since 2015, reaching their peak in 2025, with major contributions originating from highly flood-prone countries such as India, Iran, and China. In addition, Geocarto International and Biswajeet Pradhan were identified as the most cited journal source and author, respectively. Furthermore, cluster analysis reveals that the integration of the Analytic Hierarchy Process (AHP) and GIS has increasingly been combined with land-use and river discharge modeling through ensemble learning approaches, including statistical models and AdaBag Rotation Forest. On the other hand, dynamic and cloud-based automated approaches remain relatively underexplored. Network and density analyses further emphasize the importance of optimizing high spatial resolution using hybrid algorithms, such as Genetic Algorithm–Multilayer Perceptron, implemented within cloud-based platforms like Google Earth Engine (GEE) to dynamically predict the impacts of climate change on urban flooding. These findings provide a strategic direction for supporting adaptive spatial planning and strengthening climate resilience in disaster risk management.]]>
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