Lespedeza cuneata (L. cuneata) is an invasive plant that spreads aggressively across grasslands in the U.S. Southern Great Plains. It threatens native biodiversity and weakens ecosystem resilience. Understanding where and why this plant invades is key to developing better control strategies. In this study, we applied several modeling approaches (both statistical and machine learning) to examine the environmental conditions that favor the spread of L. cuneata. We analyzed how environmental factors such as water availability, soil type, forest cover percentage, and climate conditions relate to the plant’s presence. To simplify complex climate data, we used statistical methods (principal component analysis and clustering) to group climate regions based on similarities. We then analyzed the relationships between L. cuneata presence and environmental variables using the models. Our results showed that L. cuneata is most likely to invade areas with moderate rainfall and temperature, nearby water sources, and loamy soils. Cool, forested areas with silty or sandy soils were less favorable. The findings help identify high-risk areas and improve our ability to predict future invasions, thereby protecting vulnerable grasslands from invasive species.