<p>This study assessed risk of major flooding across the globe based on data in the Emergency Events Database spanning 1980 to 2023 and two machine learning methods, extreme gradient boosting (XGBoost) and random forest (RF). A flood disaster index was calculated for politically defined provinces around the world using a combination of analytic hierarchy processing (AHP) and entropy weighting (EW). The resulting indices, together with hydro-meteorological, topographic, vegetation and economic variables, were used to train two machine learning algorithms, which ranked 20 variables according to their relative contribution to flood risk in areas differing in climate zones or levels of socio-economic development. The two algorithms did not substantially differ from each other in their rankings. The modeling identified the following areas as particularly vulnerable: China, South Asia, western Arabian Peninsula, western Germany, Java (Indonesia), Zulia (Venezuela), and eastern Australia. The major determinants of major flood risk depend on the climate zone: in the tropics, economy and precipitation are major determinants; in arid regions, vegetation cover; in temperate regions, population and prolonged heavy rainfall; in cold regions, precipitation and surface soil moisture; and in polar regions, topographic factors. In the socio-economically defined "Global North", precipitation may be the primary determinant, while in the "Global South", economic factors may be more crucial. This study enhances global flood risk assessment through the integration of multi-source data and machine learning techniques, providing novel insights into regional heterogeneity in flood risk.</p>