Rewetting peatlands is required to limit carbon dioxide (CO₂) emissions, however, raising the groundwater level (GWL) will strongly increase the chance of methane (CH₄) emissions which have a higher radiative forcing than CO₂. Datasets of CH₄ from different rewetting strategies and natural systems are scarce, and quantification and an understanding of the main drivers of CH₄ emissions are needed to make effective peatland rewetting decisions. We present a large dataset of CH₄ fluxes (FCH₄) measured across 16 sites with eddy covariance on Dutch peatlands. Sites were classified into six land uses, which also determined their vegetation and GWL range. We investigated the principal drivers of emissions and gap-filled the data using machine learning (ML) to derive annual totals. In addition, Shapley values were used to understand the importance of drivers to ML model predictions. The data showed the typical controls of FCH₄ where temperature and the GWL were the dominant factors, however, some relationships were dependent on land use and the vegetation present. There was a clear average increase in FCH₄ with increasing GWLs, with the highest emissions occurring at GWLs near the surface.  Soil temperature was the single most important predictor for ML gapfilling but the Shapley values revealed the multi-driver dependency of FCH₄. Mean annual FCH₄ totals across all land uses ranged from 90±11 to 632±65 kg CH₄ ha^-1yr^-1 and were on average highest for semi-natural land uses, followed by paludiculture, lake, wet grassland, and pasture with water infiltration system. The mean annual FCH₄ was strongly correlated to the mean annual GWL (R² = 0.80). The greenhouse gas balance of our sites still needs to be estimated to determine the net climate impact, however, our results indicate that considerable rates of CO₂ uptake and long-term storage are required to fully offset the emissions of CH₄ from land uses with high GWLs.