Bangladesh is a natural hazard hotspot where research on resilience has grown rapidly but remains fragmented across social and technical silos. We assemble an auditable corpus of 301 peer-reviewed studies (2014–2024) and integrate PRISMA screening with Latent Dirichlet Allocation (LDA) to deliver a transparent, data-driven map of the field. A 10-topic model, selected by maximizing Cv coherence (perplexity as tie-break), is stress-tested via multi-seed and split-half Jensen–Shannon distance; dual-reviewer screening reports substantial agreement (Cohen’s κ). The map confirms the dominance of community adaptation and DRR governance and reveals a quantifiable social–technical separation between qualitative community studies and quantitative hazard modeling. Under-represented intersections include private-sector continuity and supply-chain resilience, and salinity–health linkages. We translate these findings into an operational blueprint: a three-layer integration architecture – participatory GIS indicators, hazard/lifeline rasters, and a Bayesian-network inference layer – to evaluate district-level policy levers (e.g., shelter capacity, road chokepoints, anticipatory cash) with uncertainty bands. We also outline a national, open data infrastructure to support reproducible, multi-hazard, uncertainty-aware planning. By coupling transparent evidence synthesis with prescriptive design, this study provides a practical pathway to close Bangladesh’s social–technical gap and advance “bounce-forward” resilience
