<p dir="ltr">The preservation of grassland resources and the enhancement of the biological environment depend on the accurate measurement of grassland aboveground biomass (AGB) over wide regions. Google Earth Engine (GEE) was used in this work to develop the models for predicting grassland AGB. These models were based on long-term, large-scale grassland AGB measurement data from the Bundelkhand area of semi-arid India, as well as data from remote sensing, topography, soil, and climate. The grassland AGB distribution pattern was created. Based on topography, soil, climate, and remote sensing data, the Extreme gradient boosting (XGBoost) model demonstrated high simulation accuracy, according to the findings. During the research period, the southern portion of Bundelkhand had a higher overall growth tendency for grassland AGB than the northern regions. Nevertheless, the Bundelkhand region's production was limited by nitrogen, phosphate, and potassium; hence, controlling these nutrients might assist increase AGB. The study's findings offer a quantitative viewpoint for investigations into central India's grassland livestock carrying capacity and climate</p>
