With the acceleration of the pace of life, the functional food market has experienced rapid growth. The emergence of artificial intelligence (AI) technology has provided innovative approaches for the functional food industry. Through a combination of literature review and case analysis, this paper systematically investigates the application of AI technology in functional food testing, process optimization, and market forecasting. The objective was to explore how state-of-the-art AI technology can be employed to optimize the research and development, production, and regulation of functional food, thereby addressing existing industry challenges, such as inconsistent product quality and a lack of innovation. Analytical findings revealed that machine learning (ML), deep learning (DL), and computer vision (CV) technologies enable rapid quality assessment (with accuracy exceeding 90%) through the analysis of physicochemical data and spectral images, significantly improving the precision and efficiency of food testing. Furthermore, AI demonstrated considerable potential in food safety regulation and personalized nutrition recommendations. However, the application of AI in the field of functional foods still faces challenges, including limited data standardization and difficulties in technological implementation. Therefore, future research should prioritize optimizing algorithmic adaptability and fostering interdisciplinary collaboration to advance the digitalization, intelligent automation, precision production, and personalization of functional foods. This study provides a valuable reference for the deep integration of AI and food science while outlining potential innovation pathways for future development.