Syncope, characterized by transient loss of consciousness (TLOC) due to brief cerebral hypoperfusion, presents significant diagnostic and management challenges. Distinguishing syncope from other TLOC causes is complex, and while clinical stratification tools exist, their adoption remains inconsistent. Artificial intelligence (AI) and machine learning (ML) offer promising advancements in syncope management. AI-driven diagnostic tools, such as random forests and natural language processing (NLP) algorithms, have demonstrated high sensitivity in differentiating syncope from other TLOC causes. In diagnostic testing, AI models like support vector machines (SVMs) effectively interpret physiological data from head-up tilt tests (HUT), enhancing the assessment of reflex syncope. For prevention, AI models utilizing physiological parameters and wearable technology have shown potential in forecasting syncope episodes, providing early warnings to prevent falls and associated injuries. Risk stratification benefits from AI’s ability to integrate extensive data, outperforming traditional risk scores like the Canadian Syncope Risk Score in predicting short-term adverse outcomes and hospital length of stay. AI models, including gradient boosting and neural networks, offer robust predictions by incorporating diverse predictors such as demographics, medical history, and clinical findings. Despite these advances, challenges include the generalizability of AI models across varied clinical settings and the need for careful integration with human clinical judgment. Effective AI adoption in syncope management requires transparent models, external validation, and collaboration between technology developers and healthcare professionals, ensuring AI enhances rather than replaces clinical decision-making.