Artificial intelligence AI is being increasingly applied in the healthcare field gradually extending to nutritional management and offering new solutions to the global challenge of malnutrition among elderly and cancer patients. Traditional nutritional management methods are often inefficient subjective and difficult to personalize. Through machine learning natural language processing and multimodal data analysis AI enables efficient and precise nutritional screening assessment intervention and monitoring. In the area of nutritional screening and assessment AI-based automated tools such as a facial image recognition model can quickly identify high-risk patients. Multidimensional data-driven predictive models contribute to more accurate determination and grading of nutritional status. In the intervention phase AI technology is used to explore the relationships between individual datagenomic microbial metabolomic and behavioral-and nutritional influences thereby designing personalized dietary and nutritional support plans. For monitoring and prognosis AI utilizes technologies such as image recognition and wearable devices to track nutritional status in real time and dynamically adjust intervention strategies. Machine learning models can also predict complications survival rates and changes in physical function based on nutritional indicators assisting in clinical prognosis evaluation. Although AI shows great potential in nutritional management it still faces challenges such as insufficient data standardization and ethical privacy concerns. Future efforts should focus on constructing high-quality multi-center datasets developing interpretable algorithms and validating clinical applications to promote the standardized and scalable use of AI in nutritional management ultimately improving patients' quality of life and health outcomes.