Wetlands can mitigate climate change through carbon sequestration, but they can also be a source of greenhouse gas emissions. To analyze these trade-offs, we used observed data from 287 wetland sites across the United States, compiled from eddy covariance tower and gas chamber datasets. Multiple machine learning algorithms (Random Forest; XGBoost; SVM - Support Vector Machine; ANN - Artificial Neural Network; and PLSR - Partial Least Squares Regression) were trained to predict gross primary productivity (GPP), ecosystem respiration (RECO), net ecosystem exchange (NEE), and methane (CH₄) fluxes using 27 environmental drivers at a monthly time scale. To account for spatial heterogeneity, models were trained by ecoregions, including High-Latitude Temperate, Mid-Latitude Temperate, SE Coastal Plains, Great Plains, Mediterranean California, and Alaska. These regional models were then used to spatially scaled up GPP, RECO, NEE and CH4 flux for the whole United States across 2000–2020 using a multi-model framework and gridded drivers. Overall, model evaluation showed that GPP and RECO models perform strongly across regions and algorithms. NEE predictions exhibited greater variability in performance but remained relatively strong, compared to RECO, GPP and CH4 models. PLSR consistently yields the weakest performance for NEE predictions. Among different regions, CH4 models perform best in Mediterranean California, with a particularly weak performance in the High-Latitude Temperate. Gridded predictions highlighted strong spatial contrasts, with localized hotspots of both carbon sequestration and emissions. Overall, our results highlight a transferable, data-driven framework for informing conservation strategies across a range of climatic settings.