DA (data assimilation) is a crucial technical method for improving the accuracy of atmospheric chemical forecasts by integrating the results of atmospheric chemistry models with multi-source observational data, reducing uncertainties in model input data. Centering on DA techniques for atmospheric chemistry models, the transformation process of initial field assimilation for pollutant gases and aerosols from single state variables to multi-state variables was systematically reviewed. Meanwhile, the important progress of pollutant emission source assimilation inversion using ensemble methods and four-dimensional variational methods was focus on the improvement of emission source accuracy, optimization of spatiotemporal resolution, and enhancement of pollutant concentration prediction performance. With the explosive growth of observational data, a core challenge in the current field lied in fully leveraging high-resolution geospatial and remote sensing data for atmospheric chemical DA. The deep integration of DA with artificial intelligence algorithms represented a key research direction to break through this bottleneck and significantly enhanced the accuracy of atmospheric composition analysis and forecasting.