Bioaerosols are one of the main types of aerosols originating from the Earth’s biosphere and are widely found in the atmosphere. They possess both biological attributes and aerosol characteristics, thereby exerting significant influences on climate, the environment, ecosystems, and public health. However, their regional-scale distribution, influencing factors, climatic and environmental impacts remain unclear due to the scarcity of observational data. This study firstly establishes an integrated bioaerosol dataset based on a large-scale dust–bioaerosol field campaign conducted across East Asia using unified sampling and analytical methods. The dataset systematically integrates atmospheric bioaerosol number concentrations and bacterial community structure at multiple taxonomic levels across 45 sites in China, Japan, South Korea, and Mongolia. In addition, meteorological variables (e.g., air temperature, relative humidity, wind speed and direction), air quality parameters (e.g., PM<sub>10</sub> and PM<sub>2.5</sub>), and Normalized Difference Vegetation Index (NDVI) data during the sampling period were incorporated from multiple sources. Further analysis of this integrated dataset indicates that bioaerosol number concentrations are negatively correlated with local NDVI. Moreover, there is a clear relationship between bioaerosol number concentration and air temperature, with a peak observed at approximately 10–15 °C. A pronounced diurnal variation in bioaerosol concentrations is also found, which is strongly associated with Aerosol Optical Depth (AOD) and particulate matter concentrations. In addition, substantial differences in community structure were observed across different underlying surface types, and the α-diversity indices (Shannon and Chao1 indices) were negatively correlated with NDVI. This dataset provides a robust foundation for advancing research on atmospheric bioaerosol processes, as well as their implications for climate, the environment, public health, and interdisciplinary studies. The dataset generated in this study is openly available via Zenodo (<a href="https://zenodo.org/records/19605145" target="_blank" rel="noopener">https://zenodo.org/records/19605145</a>) (Huang et al., 2026).