Landslide detection and mapping in remote and complex mountainous terrain is challenging, costly, and time-consuming for both disaster mitigation and land-use planning. Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) technologies, in this context, have emerged as powerful tools that enable the detection and tracking of subtle deformation signals associated with slow-moving landslides. In this chapter, slow-moving landslides in the remote and complex terrain of Pakistan using multi-orbit Sentinel-1 images acquired between January 2017 and December 2023 are mapped and investigated. We aim to map these landslides by testing and integrating three different point target detection criteria (i.e., spectral diversity, temporal variability, and phase stability) and processing them jointly for deformation modeling. We transformed average line-of-sight (LOS) deformation measurements (−60 to +60 mm/year) onto the steepest slope directions (vSLOPE), which were scanned through several GIS-based post-processing steps to identify and classify deformation hotspots, including landslides, subsidence, and erosion. Revealing the presence of approximately 2500 landslides of varying sizes up to 1 sq. km, the study also illustrates the potential of the tested method and Sentinel-1 data in detecting relatively low motion rates in remote and complex terrain that may contribute to updating the existing active landslide inventory in the region. Adhering to several factors, including precipitation as a speeding-up tool, the distribution analysis of the mapped landslides showed close relationships with distances to faults, drainages, and roads. The findings of this research have the potential to enhance regional-scale landslide risk management, and methodology can be applied to other landslide-prone regions worldwide.