Machine learning, as an advanced data processing method, has become one of the key technologies in the research of adsorption processes due to its outstanding nonlinear modeling capabilities. Its significance lies in that machine learning not only can accurately predict the adsorption process but also plays an important role in the selection and optimization of material synthesis pathways. Currently, research in the field of adsorption mainly focuses on the design of adsorbents, the optimization of adsorption processes, and the development of reactors. This paper systematically classifies the role of artificial intelligence algorithms in adsorption research and reviews the specific applications of these algorithms in the adsorption process, including the screening and design of adsorbents, the prediction and modeling of adsorption parameters, and the design and manufacture of reactors. Machine learning models are classified according to different application scenarios, covering various algorithms such as data modeling, image processing, and sequence analysis. At the same time, this paper also emphasizes the progress made in developing interpretable models for adsorption processes. Finally, the paper discusses the future potential and challenges of artificial intelligence in the field of adsorption.