Landslide prevention and mitigation techniques have gained immense attention recently due to the severity of landslide dangers increasing. Implementation of techniques for predicting landslide vulnerability has been a major area of research interest. Especially in hilly areas where there is a high risk of landslides, these prediction abilities are crucial for planning urban expansion and land exploitation plans. The study focuses on the literature review of predicting landslides by highlighting the various traditional and transfer learning techniques using Machine Learning and Deep neural network. Reviews on various algorithms are explored on how landslide prediction are done by considering landslide factors. The study also explores on how earth’s surface can be more clearly visualized spatially and spectrally by utilizing advanced technologies. In order to support efficient risk reduction and catastrophe management initiatives, the study intends to address the issues of inadequate coverage, manual data processing, and a lack of real-time monitoring in landslide detection.