Existing ways of assessing rangeland plant biomass and nutritional quality mostly rely on field surveys, which are difficult to generalise across plots, along with laboratory-based techniques that entail lengthy pre-processing procedures. As a solution, drones have emerged as a promising tool capable of collecting low-altitude images over expansive areas with minimal effort and cost. Here, we explore the potential of low-cost drone images to estimate the rangeland biomass, and quality of the Sahelian rangeland ecosystem. Model calibration and validation were conducted using a random forest machine learning algorithm, where the response variables were field vegetation samples, and the explanatory variables were derived from drone image outputs. A principal component analysis (PCA) was conducted to explore the multivariate relationships between drone-derived vegetation indices and field-measured biomass and quality attributes. In the validation datasets, the random forest model exhibited relative root mean squared errors (RRMSE) of 31% for fresh mass and 37% for dry mass. The random forest model demonstrated a relatively high prediction accuracy, yielding RRMSE values of 32% for crude protein, 9% for neutral detergent fibre, 8% for acid detergent fibre, and 17% for organic matter digestibility contents. The PCA revealed that the first two components explained 53.3% of the total variance. Overall, these results showed that red, green and blue (RGB) images acquired from low-cost drones can be used to estimate rangeland biomass and quality.