High contents of reactive nitrogen components aggravate air pollution and could also impact ecosystem structures and functioning across the terrestrial–aquatic–marine continuum. However, the long-term historical trends and future predictions of reactive nitrogen components at the global scale still remain highly uncertain. In our study, field observations, satellite products, model outputs, and many other covariates were integrated into the multi-stage machine-learning model to capture the global patterns of reactive nitrogen components during 2000–2019. In order to decrease the estimate uncertainties in the future scenarios, the constructed reactive nitrogen component dataset for the historical period was utilized as the constraint to calibrate the CMIP6 dataset in four scenarios. The results suggested that the cross-validation (CV) R² values of four species showed satisfying performance (R² > 0.55). The concentrations of estimated reactive nitrogen components in China experienced persistent increases during 2000–2013, while they suffered drastic decreases from 2013, except for NH₃. This might be associated with the impact of clean-air policies. However, in Europe and the United States, these compounds have remained relatively stable since 2000. In the future scenarios, SSP3-7.0 (traditional-energy scenario) and SSP1-2.6 (carbon neutrality scenario) showed the highest and lowest reactive nitrogen component concentrations, respectively. Although the reactive nitrogen concentrations in some heavy-pollution scenarios (SSP3-7.0) also experienced decreases during 2020–2100, SSP1-2.6 and SSP2-4.5 (middle-emission scenario) still showed more rapidly decreasing trends. Our results emphasize the need for carbon neutrality pathways to reduce global atmospheric N pollution.