The Kabudar Ahang Plain, located in Kabudar Ahang County (located in the geographical area between 35 degrees and 3 minutes to 35 degrees and 4 minutes north latitude and 48 degrees and 45 minutes east longitude), has been experiencing the most severe subsidence in recent years. In this article, radar interferometry techniques and images from the Sentinel-1 radar satellite and descent orbit images between 2018 and 2023 were used. Due to the location of the study area in different imaging tracks, two ascending and two descending paths were used, and then the radar interferometry results in both ascending and descending paths were combined (merged). In the machine learning model, along with ten layers such as DEM, slope, slope direction, curvature, distance from aquifer, distance from road, distance from river, distance from well, and NDVI and NDWI, the Shuttle Radar Topography Mission digital elevation model (SRTM) with a spatial accuracy of 90 meters was used to predict displacement. The results of the studies show that the highest subsidence rate occurred in the southeastern parts of Kabudarahang County due to the major influence of the elevation of the study area and areas with high vegetation cover. The highest subsidence occurred in the radar interferometry method and the RFR and GBR models was -3.9, -8.16, and -7.17 mm/year, respectively. The amount of subsidence calculated by machine learning models is greater than that by radar interferometry; while the difference in calculating the maximum uplift that occurred in the study area between machine learning models and radar interferometry is less. The values ​​of R2, RMSE and MAE obtained from machine learning models do not differ significantly compared to each other and the results obtained from these models have high accuracy.