Metabolic-bariatric surgery (MBS) has become an important treatment for pathological obesity and metabolic diseases. However, common postoperative nutritional complications—such as protein-energy malnutrition, iron deficiency anemia, and vitamin B12 deficiency—significantly affect patients' long-term prognosis. Traditional nutritional management models rely on static monitoring and standardized supplementation, which are insufficient to address individual variability and dynamic postoperative changes. Artificial intelligence (AI), through integrating multimodal data (such as biochemical indicators, imaging information, and wearable device monitoring) and intelligent modeling, offers new approaches for dynamic monitoring, risk prediction, and personalized intervention. Based on literature from 2017 to 2025, this article systematically evaluates the application of AI in perioperative nutritional management for MBS, covering key technologies including machine learning, deep learning, and natural language processing. It also analyzes current challenges in clinical translation, such as data fragmentation, lack of model interpretability, and limited long-term validation. In the future, enhanced multi-center collaboration, the development of standardized databases, and explainable models will be essential to advancing nutritional management in MBS from empirical practice to precision medicine.