Andean communities in central Peru play a key role in the conservation of vicuñas (Vicugna vicugna), a protected species that depends on puna grass and flooded vegetation for food and access to water throughout the year. This study focuses on seven communities of Lucanas in Ayacucho, a dry mountainous region of Peru, emphasizing the need for accurate information to monitor resources in a context of climate change and support community decision-making. In this research, based on Google Earth Engine (GEE), we evaluated the performance of classification algorithms using Sentinel-1 (S1) and Sentinel-2 (S2) image data for rangeland classification. The process used ground-based and image-based points to train and validate the models, a filter to minimize spatial autocorrelation between training and validation sets, and spectral separability measurements using the Jeffries–Matusita (JM) distance. All of these steps allowed for adequate discrimination and representation of the classes. Additionally, we used 64 feature variables (including vegetation, texture, topographic, snow, water, mineral, and radar features) and applied Cloud Score+, a quality assessment (QA) processor for S2 image collection, to improve classification accuracy. The Random Forest (RF) algorithm achieved an overall accuracy (OA) of 92% and a Kappa coefficient of 0.908, outperforming the Support Vector Machine (SVM) algorithm, which obtained an OA of 90.9% and a Kappa coefficient of 0.895. The results show that, in the semi-captivity sectors, 1,777.5 hectares of puna grass and 319.1 hectares of flooded vegetation were identified, while in wild management areas, 5,431.1 hectares of puna grass and 843.8 hectares of flooded vegetation were recorded. These findings highlight the importance of integrating remote sensing tools and machine learning algorithms to generate key information in the management of natural resources in communities.