Ecosystem services (ESs) indicate that species comprising an ecosystem can provide services that are significant to human beings. ES classification of vegetation is significant for accelerating ecological restoration and achieving sustainable development. However, current vegetation classification product cannot effectively reflect their ESs. This study focused on the north Tianshan Mountain, Xinjiang, China, and tried to classify ESs of vegetation based on machine learning and remote sensing techniques. This study utilized multiband Landsat-8 surface reflectance data from Google Earth Engine (GEE) together with meteorological data and Digital Elevation Model data. The classification map accurately portrayed the spatial distribution of seven types of vegetation ES, including agricultural production (AP) cropland, sandstorm prevention (SP) and water retention (WR) forest, grassland, and shrub. The classification results indicated that, the two types of grassland occupy the largest area besides desert, followed by AP cropland, whereas SP forest and WR shrub have the smallest area. The accuracy of ES classification for vegetation is very high, with an impressive overall accuracy of 89.61% and a kappa coefficient of 0.874. This study provides valuable insights into ES classification of vegetation, which can serve as a reference for formulating an ES development plan in north Tianshan Mountain, Xinjiang.