Streams are important freshwater habitats in large-scale carbon budgets because of their high CO₂ fluxes which are driven by high CO₂ concentrations and surface-water turbulence. High CO₂ concentrations are promoted by terrestrial carbon inputs, groundwater flow, and internal respiration, all of which vary greatly across space and time. We used environmental monitoring data to calculate CO₂ concentrations along with a wide range of predictor variables including outputs from a national hydrological model and trained machine learning models to predict spatially distributed seasonal CO₂ concentrations in Danish streams. We found that streams were supersaturated in dissolved CO₂ (mean = 118 μM) and higher during autumn and winter than during spring and summer. The best model, a Random Forest model, scored R² = 0.46, MAE = 46.0 μM, and ⍴ = 0.72 on a test set. The most important predictor variables were catchment slope, seasonality, height above nearest drainage, and depth to groundwater, highlighting the importance of landscape morphometry and soil-groundwater-stream connectivity. Stream CO₂ fluxes determined from the predicted concentrations and gas transfer velocities estimated 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.