<![CDATA[This rapid scoping review maps geographic information systems (GIS), Internet of Things (IoT), and machine learning (ML) approaches for flood information and further research in West Java. A Population–Concept–Context framework guided targeted Consensus and publisher searches, with a literature cutoff of October 2025. Crossref metadata and DOI resolution were checked. Thirteen studies were mapped: six local studies and seven purposively selected comparators; nine were read in full and four through abstracts. GIS and ML each appeared in nine studies, and IoT in four. No mapped study reported operational integration of all three. Local evidence distinguished coastal tidal inundation, downstream river flooding, urban susceptibility, and Upper Citarum modelling. Data priorities included tidal boundaries, elevation, channel geometry, rainfall timing, and event inventories. The proposed architecture separates mapping, observation, prediction, and information delivery. Findings guide data selection without establishing operational readiness. Targeted retrieval, abstract-only records, and the absence of duplicate screening limit completeness.]]>
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