Radiometric vegetation indices are considered good indicators of vegetation health and can contribute to explaining its current and future evolutions. This study is carried out in the arid mountain rangeland of Toujane (southeast of Tunisia). The aim is to predict how climate change will affect the Soil-Adjusted Vegetation Index (SAVI) values under dryland conditions. Current and future SAVI indices are analyzed using the maximum entropy algorithm (MaxEnt). The Canadian Earth System Model version 5 (CanESM5) represents the data source of two future climatic scenarios. These last, called Shared Socioeconomic Pathways (SSP245, SSP585), concern four time periods (2021-2040, 2041-2060, 2061-2080, and 2081-2100). Three topographic, twelve soil, and nineteen climatic variables are undertaken during each period. The main results of the jackknife test show that temperature, precipitation, and some soil variables are the main factors influencing SAVI indices. Specifically, they affect plant growth and vegetation cover, which in turn modify the SAVI index. Based on the area under the receiving curve, the model shows high predictive accuracy for a high SAVI (AUC = 0.88 - 0.92). These findings show that land management strategies may be incumbent upon to reduce the vulnerability linked to climate change in Toujane rangelands.