Yellow sweetclover (Melilotus officinalis; YSC), an invasive biennial legume, bloomed throughout the Northern Great Plains (NGP) following greater-than-average precipitation during 2018-2019. YSC can increase nitrogen (N) levels and potentially cause broad changes in the composition of native plant species communities. There is little knowledge of the drivers behind its spatiotemporal variability, including conditions causing significant widespread blooms across western South Dakota (SD). We aimed to develop a generalized prediction model to map the relative abundance of YSC in suitable habitats across rangelands of western SD for the recent sweet clover year 2019. The following research questions were asked: 1. What is the spatial extent of YSC across western SD? 2. Which model can accurately predict the habitat and percent cover of YSC? and 3. What environmental drivers affect its presence across western SD? We trained machine learning models with in-situ data (2016-2021), Sentinel 2A-derived surface reflectance and indices (10m and 20m) and site-specific variables (e.g., climate, topography, land cover, and edaphic factors) to optimize model estimates. Our study identified the most suitable drivers to explain the variability in YSC presence and its percent cover through data dimensionality reduction techniques. Our research demonstrated how machine learning algorithms could help generate valuable information on the spatial distribution of invasive rangeland plant species. We found major YSC hotspots in Butte and Meade counties of SD. The floodplains of major rivers in SD, such as the Cheyenne, White, and Bad Rivers, also showed a higher occurrence probability and percent cover range. These prediction maps could aid land managers in devising strategies for regions that are prone to YSC overruns. This management workflow can serve as a prototype for mapping other invasive plant species worldwide.