Streams are important freshwater habitats in large-scale CO2 emissions budgets because they are generally supersaturated with dissolved CO2. High CO2 concentrations driven by terrestrial carbon inputs, groundwater flow, and internal respiration vary greatly across space and time. We compiled and used environmental monitoring data to calculate CO2 concentrations along with a wide range of predictor variables and trained machine learning models to predict spatially distributed seasonal CO2 concentrations in Danish streams. We included outputs from a national hydrological model to investigate the influence of hydrological processes. We found that CO2 concentrations in streams were supersaturated (mean = 118 µM) and higher during autumn and winter than during spring and summer. The best model, a Random Forest model, which scored R2 = 0.46, MAE = 46.0 µM, and ⍴ = 0.72 on a test set, predicted seasonal CO2 concentrations for the entire stream network. The most important predictor variables were catchment slope, seasonality, elevation relative to the nearest stream, and depth to groundwater, which highlights the importance of landscape morphometry and soil-groundwater-stream connectivity. Stream CO2 fluxes, calculated by using empirical relationships, averaged 253 mmol m-2 d-1, and the annual emissions were 513 Gg CO2 from the national stream network (area = 139 km2). Our analysis presents a framework for modeling seasonal CO2 concentrations and estimating fluxes at a national scale by means of large-scale hydrological model outputs. Future efforts should consider further improving the temporal resolution, direct measurements of fluxes and gas transfer velocities, and seasonal variation in stream surface area.