Accurate and timely estimation of grass yield is crucial for understanding the ecological conditions of grasslands in the Mongolian Plateau (MP). In this study, a new artificial neural network (ANN) model was selected for grassland yield inversion after comparison with multiple linear regression,K-nearestneighbor,andrandomforestmodels. TheANNperformedbetterthantheother machine learning models. Simultaneously, we conducted an analysis to examine the spatial and temporalcharacteristicsandtrendsofgrassyieldintheMPfrom2000to2020. Grasslandproductivity decreased from north to south. Additionally, 92.64% of the grasslands exhibited an increasing trend, whereas 7.35% exhibited a decreasing trend. Grassland degradation areas were primarily located in Inner Mongolia and the central Gobi region of Mongolia. Grassland productivity was positively correlatedwithlandsurfacetemperatureandprecipitation,althoughthelatterwaslesssensitivethan the former in certain areas. These findings indicate that ANN model-based grass yield estimation is an effective method for grassland productivity evaluation in the MP and can be used in a larger area, such as the Eurasian Steppe.