<p>Anthropogenic nitrogen fluxes into surface freshwater bodies significantly impair water quality (WQ), pose serious health hazards, and create critical environmental threats. Quantification of the magnitude and impact of WQ issues requires identifying the key controls of nitrogen dynamics and assessing past and future patterns of global nitrogen flows. To achieve this, we adopted a data-driven, machine learning approach to build a space-time random forest model for simulating nitrogen concentration in 115 major river basins of the world. The proposed random forest-based WQ model regressed the monthly measured nitrogen concentration collected at 718 river stations across the globe for the period of 1992–2010 onto a set of 17 predictor variables with a spatial resolution of 0.5-degree. The resulting model was validated with data from river basins outside the training dataset, and was used to predict nitrogen concentrations in all river basins globally, including many with scarce or no observations. We predict that the regions with highest median nitrogen concentrations in their rivers (in 2010) were: United States, India, Pakistan, Bangladesh, China, and most of Europe. Furthermore, our results showed that the rate of increase between 1990s and 2000s was greatest in rivers located in eastern China, eastern and central parts of Canada, Baltic states, southern Finland, Pakistan, parts of Russia, mainland southeast Asia, and south-eastern Australia. We found that, globally, the most influential predictors of nitrogen concentrations are temporal: month of the year and cumulative month count, reflecting the secular trend. Apart from temporal variables, cattle density, nitrogen fertilizer application, temperature, precipitation, and pig population are the most influential predictors of nitrogen pollution of the river systems. The proposed global WQ model will provide a new tool to explore agricultural and land management strategies designed to reduce nitrogen pollution in freshwater bodies at large spatial scales.</p>