In-stream water residence time (WRT) in river networks is a crucial driver for biogeochemical processes in riverine ecosystems. Dynamics of the WRT are critical for understanding and modelling nutrient retention in lakes and rivers, in particular during flood events when riparian areas are inundated. This study illustrates the potential utility of integrating spatial landscape analysis with machine learning statistics to understand the effects of hydrology and geomorphology on WRT in river networks, especially at large scales. We applied the Boosted Regression Trees (BRT) approach to estimate water residence, a promising multi-regression spatial distribution model with consistent cross-validation procedure, and identified the crucial factors of influence. Reach-average WRTs were estimated for the annual mean hydrologic conditions as well as the flood and drought month, respectively. Results showed that the three most contributing factors in shaping the WRT distribution are river discharge (57 %), longitudinal slope (21 %), and the drainage area (15 %). This study enables the identification of key controlling factors of the reach-average WRT and estimation of WRT under varying hydrological conditions. The resulting distribution model of WRT is an easy to apply and sound approach helping to improve water quality modelling at larger scales and water management approaches aiming to estimate nutrient fluxes in river systems.