Traditional soil salinity studies, especially over large areas, are expensive and time-consuming. Therefore, it is necessary to employ new methods to examine salinity of large areas to reduce the time and cost of analysis. This study investigates soil salinity trends in the Yazd-Ardakan plain of Iran using remote sensing with emphasis on historic and projected land use and groundwater change between 1986 and 2030. A random forest model was used to estimate soil salinity. To predict the salinity of the Yazd-Ardakan plain in 2030, the relationships between soil and auxiliary data from 2016 were used. Land use parameters and groundwater quality parameters that are projected to change by 2030 were selected. A sensitivity analysis of a forage management model was conducted in conjunction with soil salinity modeling and the most important auxiliary data were found to be groundwater parameters and digital elevation derivatives of vegetation indices. Based on 10-fold cross-validation, random forest model predicted soil salinity with R2 value of 0.73. Comparison of soil salinity trends from 1986 to 2016 shows that during this period the size of the area with salinities in the range of 4–8 dS/m and >32 dS/m were increased from 1.6 to 3.1% (~1.5%↑) and from 13.1 to 18.3% (~5.1%↑), respectively. However, the size of the fairly high (8–12 dS/m), high (12–16 dS/m) and very high (16–32 dS/m) classes were decreased from 13.6 to 11.9% (~1.7%↓), from 20.2 to 16.5% (~3.8%↓), and from 50.2 to 49% (~1.1%↓) , respectively. In other words, it can be said that during this 30-years, we see an increase in salinity levels and a decrease in soil quality. The results of the changes in soil salinity show that between 2016 and 2030, the area of the class with >32 (dS/m) (43159.2 ha, 8.83%↑) increased, while the class with <4 (dS/m), was eliminated in 2030 and the 4–8 (dS/m) class is on the verge of disappearing. The trend of salinity changes in the region shows an increase from east to west, which is consistent with the trend of changes in the most important ancillary variables identified.