In this study, the capabilities of Interferometric Synthetic Aperture Radar (InSAR) time series data and machine learning have been evaluated for land cover mapping in Iran. In this way, a time series of Sentinel-1 SAR data (including 16 SLC images with approximately 24 days time interval) from 2018 to 2020 were used for a region of Ahvaz County located in Khuzestan province. Using InSAR processing, 25 coherence images were obtained based on different SAR pairs. Five dominant land cover classes in the region including built-up lands, agricultural lands, water bodies, bare soil, and dense natural vegetation cover were identified and considered. Through Google Earth's high-resolution imagery, a total of 4,930 ground truth samples with appropriate spatial distribution were acquired for all classes. The obtained multi-temporal coherence images were used as input variables to the support vector machine (SVM) classifier. The training and validation process of different SVM kernels was performed using 80% and 20% of the ground truth samples, respectively. Overall accuracy in different kernels including linear, 2th-degree polynomial, 4th-degree polynomial, 6th-degree polynomial, radial base function (RBF), and sigmoid were computed 60.7, 64.7, 67.7, 69.9, 66.3, and 59.5%, respectively. Likewise, Kappa coefficients were reported 50. 8, 55.87, 59.62, 62.38, 57.87, and 49.38%, respectively. Accordingly, the highest and lowest overall accuracy and Kappa coefficient were belong to the 6th-degree polynomial and sigmoid kernels, respectively. Based on the user and producer accuracy assessments in all kernels, the built-up lands has the highest accuracy (93%–up to 98.5%), and in opposite the dense vegetation has the lowest accuracy (11%–up to 56.25%). Generally, the results emphasize the high potential of Sentinel-1 InSAR coherence data in land cover mapping. Meanwhile, the contribution of the classifier to the efficiency of data is also important.