Submarine landslides pose significant hazards due to their potential to generate destructive tsunamis, making their study crucial for risk assessment and mitigation. These mass wasting events are particularly prevalent in submarine volcanic island settings where oversteepened slopes, seismic activity, and oceanic processes can precondition slopes for failure. However, landslide susceptibility in such environments remains poorly understood, especially in remote oceanic regions where high-resolution data is difficult to obtain. This is particularly true for the South Sandwich Islands, a remote volcanic arc in the Southern Atlantic Ocean, which is known to be susceptible to landslide occurrence and tsunami generation, yet landslide distribution and susceptibility in this area have not been previously investigated. This study presents the first detailed landslide inventory and statistical susceptibility model focused on the South Sandwich Islands, integrating shipboard bathymetry data with multiple geologic, geomorphological and oceanographic factors using the frequency ratio (FR) approach. The resulting landslide susceptibility map exhibited good performance, with area under the curve values of 0.76 and 0.78 for success and prediction rates (PR), respectively. The results identify northward current velocity as the most influential factor preconditioning slopes for failure (PR = 3.43), and slope (PR = 1.40) and aspect (PR = 1) as the least influential. This study increases our understanding of landslide occurrence patterns and causal factors, thereby providing a foundation for improved hazard assessments aimed at mitigating the risks posed by landslide-induced tsunamis in the South Sandwich Islands and comparable submarine volcanic environments. Moreover, this study showcases the effectiveness of integrating geospatial datasets within the FR statistical modeling framework to investigate hazards in data-limited marine regions.