PM_2.5, PM₁₀, sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO), and ozone (O₃) are six fundamental pollutants in atmospheric pollution, posing significant threats to human health and the ecological environment. Given the high spatiotemporal heterogeneity of atmospheric pollutants, there is a lack of in-depth exploration into the fine spatial variations and interactions among multiple atmospheric pollutants, at ultra-high spatiotemporal resolution. In addition, most of the current studies are single-pollutant predictions, which are deficient in time and resource consumption compared with multi-pollutant synergistic predictions. To address these issues, we integrated ground-measured pollutant data, top-of-atmosphere (TOA) radiation remote sensing data, Baidu Street View data, reanalysis data, and other relevant spatiotemporal data. Using a multi-output extremely randomized trees model, we collaboratively predicted six atmospheric pollutants, generating an ultra-high-spatiotemporal-resolution (temporal resolution: hourly; spatial resolution: 100 meters) dataset of atmospheric pollutants in Wuhan, where air quality still suffers from concentration exceedances in the year 2023. The ten-fold cross-validation R² for PM_2.5, PM₁₀, O₃, NO₂, CO, and SO₂ models were 0.71-0.95, respectively. Synergistic prediction models consume only one-fifth the time of single prediction models. The spatiotemporal analysis revealed that among them, the annual average values of PM_2.5 and PM₁₀ exceeded the first-level concentration limits in China. In addition, the annual average values of pollutants have obvious spatial and temporal heterogeneity, showing a distinct spatial pattern of higher concentrations in urban centers and decreasing outwards. Correlation analyses on annual and hourly scales showed some correlation between atmospheric pollutants. In addition, the correlation between them was found to be dynamic over time at the hourly scale. These findings provide a comprehensive, high-resolution dataset of atmospheric pollutants in Wuhan, offering valuable insights into their spatiotemporal distribution and interactions.