Abstract: The intersection of artificial intelligence (AI) and wearable technology has opened new avenues for dietary management through real-time monitoring and adjustment of nutrient intake. This paper explores the development and implementation of AI-powered dietary interventions using wearable devices, which offer personalized nutritional guidance by continuously analyzing physiological data. Wearable devices, such as smartwatches and specialized sensors, track various health metrics including physical activity, glucose levels, and metabolic responses. AI algorithms process this data to provide real-time dietary recommendations, addressing individual nutritional needs with high precision. The study reviews current technologies, evaluates the effectiveness of existing AI-driven interventions, and identifies key challenges such as data privacy, sensor accuracy, and algorithmic reliability. Case studies demonstrate the potential of these technologies in managing conditions like diabetes and supporting weight loss. Promising outcomes, issues related to data security and the need for further validation highlight the need for ongoing research. This paper concludes that while AI-powered wearable devices represent a significant advancement in personalized nutrition, future efforts must focus on improving technology and addressing ethical considerations to optimize their impact on dietary health.