Detection of landslides is an important part of disaster management, as it permits the government to act swiftly to reduce the damage resulting from landslides. The possibility for landslide detection to save lives, maintain ecological balance, and optimize the use of limited resources and physical infrastructure is enormous. If government officials are able to spot landslides early on, they can take steps to lessen their impacts. Manual monitoring is commonly used in traditional landslide detection techniques, but this takes time and a lot of manpower. In contrast, AI-based landslide detection has significant benefits over conventional techniques. Because AI algorithms are capable of processing vast amounts of data in real-time, they can be used to detect and respond to landslides much more quickly. This literature review looks at how AI can be used to analyse satellite images for detecting landslides. Landslide-prone locations can be identified with the help of AI, which can scan massive volumes of satellite imagery data, segment the data, and extract the features that are important. Several artificial intelligence (AI) methods, such as Machine Learning (ML) and Deep Learning (DL) algorithms, are discussed in this paper. Several challenges associated with putting an AI model to work on satellite images are also covered. In conclusion, this study is going to be of interest to scholars and practitioners in the fields of disaster management and geospatial analysis since it gives helpful insights into the implementation of AI algorithms for landslide identification