Landslides are the most common natural disasters in mountainous areas that follow major seismic events, volcanic activity, melting snow, or prolonged and intense rainfalls and cause severe disruptions to ecosystems, economies, and societies worldwide. Therefore, minimizing their negative effects through landslide-susceptibility assessment is essential. In this study, the standard support vector regression (SVR) integrated with the gray wolf optimizer (GWO) and particle swarm optimization (PSO) algorithms were used to map landslide-prone areas. The landslide inventory map was automatically generated using a pixel-based technique based on residual U-Net algorithm from the Sentinel-2 data. In total, 4900 landslide samples were identified and divided randomly into two groups, creating training (70%) and testing (30%) datasets. In addition, nine factors that affect landslides were selected to construct a model using each algorithm. Finally, the performance of the models (SVR, SVR-GWO, and SVR-PSO) were validated and compared using the area under the receiver operating characteristic curve. The findings showed that the hybrid SVR-GWO model performed better than the standard model and is recommended for landslide susceptibility assessment due to its accuracy and efficiency.