The underutilization of remote sensing technology has compromised sustainable forage resource management, impeding the progress of livestock farmers in the northern Peruvian highlands. To accurately predict forage biomass in six high-altitude (2600-2800 m) ryegrass (Lolium multiflorum Lam) -clover (Trifolium repens) paddocks, we applied machine learning models implemented in Google Earth Engine using spectral indices derived from UAV-based multispectral imagery captured by a Micasense RedEdge MX camera mounted on a DJI Matrice 600. A total of 75 forage samples were collected from precisely geo-referenced plots to train and validate machine learning models based on 13 spectral indices. The Random Forest (RF) model, comprising 500 trees for green forage and dry matter, demonstrated high accuracy and efficiency. UAV-based biomass prediction using GEE and ML techniques was validated, achievi...