In today's landscape, where digital transformation profoundly impacts the food and gastronomy sector, AI-powered menu recommendation systems play a critical role in shaping personalized nutrition experiences. This section examines menu recommendation systems at the intersection of digital gastronomy and artificial intelligence through a comprehensive literature review perspective. The study systematically explores the applications of machine learning and deep learning methods in food recommendation, key recommendation system techniques such as collaborative filtering and content-based filtering, the advantages of hybrid approaches, and the development of personalized nutrition systems. The literature analysis highlights how AI technologies are applied across a wide spectrum, from restaurant management to individual dietary planning, while also discussing the challenges, opportunities, and future research directions in this field.