Earthquakes are among the most significant triggering factors for landslides in susceptible regions, often affecting vast areas surrounding the epicenter. Landslides pose serious threats to human lives, infrastructure, and economic assets, making their identification and systematic study a critical necessity. On November 9, 2021, a dual earthquake sequence with moment magnitudes of 6.4 and 6.0 struck northwest of Bandar Abbas, southern Iran, affecting approximately 5,000 km². This region is particularly suitable for landslide investigation due to the presence of the Sirjan-Bandar Abbas highway, railway lines, numerous villages, and a densely populated area exposed to high landslide risks. This study aims to identify and analyze landslides triggered by the 2021 Bandar Abbas dual earthquakes using Synthetic Aperture Radar Interferometry (InSAR) and RGB color compositing techniques. Surface displacements were investigated through processing interferograms and coherence maps derived from satellite radar images acquired before, during, and after the earthquake events. The RGB color compositing method was further applied to enhance the visual detection of landslide-affected areas. To validate the InSAR and color compositing results, a comprehensive landslide susceptibility map was developed. This susceptibility map integrates multiple conditioning factors, including elevation, slope angle, aspect, topographic form, structural resistance, distance to active faults, distance to roads, distance to drainage networks, and vegetation cover. To ensure high accuracy in the susceptibility mapping, most of these thematic layers were generated using photogrammetric techniques based on historical aerial stereo image pairs acquired prior to the earthquake. The Structure from Motion (SfM) method was employed to extract detailed topographic and geomorphological information from these archival images. This approach provided a pre-earthquake baseline, allowing for a more reliable assessment of earthquake-induced changes. The final landslide inventory was compiled by cross-referencing the results from InSAR analysis, RGB color compositing, and the susceptibility map, followed by visual interpretation of pre- and post-event high-resolution imagery available in the Google Earth archive. Through this integrated methodology, a total of 24 debris slide-type landslides were successfully identified across the study area. The spatial distribution analysis reveals that the majority of these landslides are concentrated in high-risk zones, particularly in close proximity to road networks, river channels, and residential areas. Furthermore, nearly all identified landslides occur on slopes exceeding 20 degrees, indicating a strong topographic control on slope instability under seismic triggering. The findings of this study demonstrate that the combined use of InSAR, RGB color compositing, and SfM-based photogrammetry provides a robust framework for post-earthquake landslide detection, even in the absence of pre-event LiDAR or high-resolution topographic data. The resulting landslide inventory and susceptibility map can support hazard mitigation planning, infrastructure protection, and risk management in earthquake-prone mountainous regions.