 This study aimed to understand the distribution and shifts of invasive alien plants, IAPs, in the Huening's catchment, South Africa using three machine learning species distribution modeling, SDM, techniques, random forest, RF, maximum entropy, max-end, boosted regression trees, BRT. The current and future bio-climatic variables, environmental and sentinel, two multispectral instrument satellite data were used to fit the models. The results show that IAPs are predicted to expand under climate change in the catchment with riparian zones, bare areas, and native vegetation being greatly affected. The mean diurnal range, Bio2, warmest quarter maximum temperature, Bio5, and warmest quarter precipitation, Bio18, were the most important bio-climatic variables in modeling the spatial distribution of IAPs. All models were successful in predicting the potential distribution of IAPs for all scenarios, with BRT, max-end, and RF having an area under curve, AUC, of 0.89, 0.92, and 0.94, respectively. The study highlights the importance of multi-source data and multiple predictive models in predicting current and potential future IAP distribution, providing baseline information for effective land management, planning, and continuous monitoring of the further spread of IAPs within the Huening's catchment. This article was authored by Pongolium Tenwana, Timothy Jub, Besta Tawona, Muderary, and others. We are article.tv, links in the description below.