Accurate quantification of the geospatial distribution of livestock in pastoral regions is important for assessing and maintaining grassland ecological security and sustainable development. Statistical livestock data based on static and macro-level administrative units cannot characterize the fine-scale distribution of livestock across mobile geographic spaces. This study proposed a livestock spatial mapping framework that combined livestock inventory statistics of soum/banner counties with multi-source data (e.g., land cover, population, topography, and climate, etc.) using the Random Forest model (RF). A series of high-resolution gridded spatial distribution datasets of total livestock, sheep & goats, and large livestock (cattle, horses, and camels) densities at five-year intervals were obtained for the Mongolian Plateau from 2000 to 2020. The fitting accuracy of this dataset with statistical data (R²>0.85) is significantly better than that of the existing Gridded Livestock of the World (GLW) series dataset, and the spatial distribution is more accurate and detailed. At the same time, it also compensates for the lack of spatial information of large livestock such as camels in the GLW. This approach enables coarse-grained administrative division data transforming into high-resolution spatial gridded data, by solving the key problems of low spatial resolution, missing local details, and the spatial fusion of different data sources. Based on the acquired high-precision spatial distribution data of livestock density, it can be fused and analyzed with other geographic environment data, which is of great value for the ecological environment protection of grassland in nomadic grassland areas. Gridded livestock density datasets are freely available at <a href="https://doi.org/10.6084/m9.figshare.28695728" target="_blank" rel="noopener">https://doi.org/10.6084/m9.figshare.28695728</a> (Liu and Wang, 2025).