Flood-risk screening is often limited by the lack of globally consistent hazard layers, because detailed hydraulic models require local calibration, boundary conditions, and substantial computation. This study presents a light data framework that uses application programming interfaces to assemble global elevation, hydrography, and road-network data for flood-hazard susceptibility mapping. Height Above Nearest Drainage is derived with drainage calibration constrained by OpenStreetMap hydrography, while the Multiresolution Index of Valley-Bottom Flatness adds complementary information on valley planarity. An interpretable monotonic gradient-boosted regression model is trained on return-period inundation inventories and converted into a common five-class ordinal hazard scale using threshold sets suited to planar and incised river settings. Applications to three independent river reaches show that the upper hazard classes consistently capture the 100-year flood footprint. The ordinal ranking remains physically coherent across return periods: low-susceptibility terrain is largely insensitive to increasing flood severity, whereas higher classes show progressively greater inundation likelihood. The model distinguishes flooded from non-flooded terrain in 78–83 % of pairwise comparisons and remains effective under strict false-alarm constraints. The framework delivers screening-grade hazard layers for prioritisation and for integration with exposure and vulnerability analyses. Future work should test broader climatic and geomorphic settings and refine transferability across regions.