Abstract Secondary organic aerosols (SOA) are formed from oxidation of hundreds of volatile organic compounds (VOCs) emitted from anthropogenic and natural sources. Accurate predictions of this chemistry are key for air quality and climate studies due to the large contribution of organic aerosols to submicron aerosol mass. Currently, only explicit models, such as the Generator for Explicit Chemistry and Kinetics of Organics in the Atmosphere (GECKO‐A), can fully represent the chemical processing of thousands of organic species. However, their extreme computational cost prohibits their use in current chemistry‐climate models, which rely on simplified empirical parameterizations to predict SOA concentrations. This study demonstrates that machine learning can accurately emulate SOA formation from an explicit chemistry model with an approximate error of 2%–8%, up to five days for several precursors and for potentially up to one month for recurrent neural network models, and with 100 to 100,000 times speedup over GECKO‐A, making it computationally useable in a chemistry‐climate model. We generated the training data using thousands of GECKO‐A box simulations sampled from a broad range of initial environmental conditions, and focused on three representative SOA precursors: the oxidation by OH of two anthropogenic (toluene, dodecane), and the oxidation by O 3 of one biogenic VOC ( α ‐pinene). We compare several neural models and quantify their underlying uncertainty and robustness. These are promising results, suggesting that neural network models could be applied to predict SOA in chemistry‐climate models, limited however to the range of environmental conditions that were considered in the training datasets.