<p>Southeast Asian countries are prone to frequent landslides at varying scales and magnitudes. History evinces the destructive nature of landslides, causing socioeconomic and environmental concerns. Early detection and warning are the primary defense mechanism to fight such disasters. Though Artificial Intelligence (AI) is deemed a way forward from recent literature, a systematic understanding and applicability in landslide susceptibility and analysis must be made more explicit. This paper focuses on this gap with a state-of-the-art review covering the current status of AI in assessing and predicting landslides, emphasizing Malaysia and Vietnam landslides. Based on 95 papers selected through a rigorous literature search and review, it is found that the Support Vector Machine, Dempster-Shafer theory, and adaptive neuro-fuzzy inference systems show better performance for landslide events from Malaysia. In comparison, the tree-based learning algorithms work well for landslide events from Vietnam. The review outcome also enlightens the application of Geographic Information Systems and remote sensing technology, combined with AI, improves the performance of landslide risk detection and prediction performance. Nevertheless, the implementation of AI is highly variable depending on the quality of data collected from past landslide events, the algorithm’s architecture, and data preprocessing methods. To this end, exploring the variation of landslide risk prediction outcomes, aided by real-time monitoring of slopes with AI, are likely the future in achieving more robust landslide early detection and warning systems. Such systems will ensure safe and reliable monitoring, detection, and prediction of landslides by facilitating timely warnings to society and concerned authorities.</p>