This study integrates the MaxEnt ecological niche model with a programmatic analysis framework developed in R to predict the potential distribution and spatiotemporal dynamics of Eriochloa villosa in spring maize fields across Northeast China under various future climate scenarios. Based on 201 validated occurrence records, key environmental variables were selected using multivariate correlation analysis in R. Model parameters were objectively optimized using the ENMeval package, and distribution maps were generated for the baseline period (current conditions) as well as for the 2030s and 2050s under three Shared Socioeconomic Pathways (SSP126, SSP245, SSP585). The results indicate that: (1) Annual precipitation (53.9%) and the mean temperature of the wettest quarter (33.8%) were the most influential variables, with optimal ecological ranges of approximately 500–900 mm and 21–27 °C, respectively. (2) Under current conditions, suitable habitats are concentrated in central Liaoning, northwestern Jilin, and eastern Heilongjiang, covering approximately 396,000 km². (3) Future scenarios project a significant habitat expansion (up to 195%) with spatial transitions characterized by a ”retreat of low-suitability zones, expansion of medium-suitability zones, and fluctuations in high-suitability zones,” alongside a westward shift and relative eastward stability. (4) The R-based standardized workflow enhanced model efficiency, objectivity, and reproducibility in species distribution modeling under climate change. These findings provide scientific support for proactive management of E. villosa and offer a reproducible technical approach for modeling the distribution of invasive or agricultural weed species under environmental change.