Constructed aquatic ecosystems such as reservoirs, aquaculture ponds, ditches, and canals are globally important sources of greenhouse gases (GHG). Yet measuring GHG emissions from freshwater ecosystems—characterized by high temporal and spatial variability—is cost-intensive and time-consuming. While several predictive models have been proposed, most are either proprietary or rely on predictors that are not universally available. Advances in data processing and the transformative capabilities of artificial intelligence (AI) present an opportunity to address these complex environmental challenges and make more informed inferences at the global scale. Here, we show how publicly available global datasets can be leveraged to develop machine-learning models capable of predicting carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) fluxes from the water surfaces of constructed aquatic ecosystems. These models can be used to target high emitting reservoirs for mitigative management as well as to inform the placement of new constructed aquatic ecosystems at the global scale.