Martian chaos terrains are fractured depressions consisting of block landforms that are often located in source areas of outflow channels. Numerous chaos and chaos-like features have been found on Mars; however, a global-scale classification has not been pursued. Here, we perform recognition and classification of Martian chaos using imagery machine learning. We developed neural network models to classify block landforms commonly found in chaos terrains—which are associated with outflow channels formed by water activity (referred to as Aromatum-Hydraotes-Oxia-like (or AHO) chaos blocks) or with geological features suggesting volcanic activity (Arsinoes-Pyrrhae-like (or AP) chaos blocks)—and also non-chaos surface features, based on >1400 surface images. Our models can recognize chaos and non-chaos features with 93.9% ± 0.3% test accuracy, and they can be used to classify both AHO and AP chaos blocks with >89 ± 4% test accuracy. By applying our models to ~3150 images of block landforms of chaos-like features, we identified 2 types of chaos terrain. These include hybrid chaos terrain, where AHO and AP chaos blocks co-exist in one basin, and AHO-dominant chaos terrain. Hybrid chaos terrains are predominantly found in the circum-Chryse outflow channels region. AHO-dominant chaos terrains are widely distributed across Aeolis, Cydonia, and Nepenthes Mensae along the dichotomy boundary. Their locations coincide with regions suggested to exhibit upwelling groundwater on Hesperian Mars.