With the rapid development of artificial intelligence (AI) technology, the food industry is accelerating its transformation toward intelligence in areas such as quality control and supply chain optimization; however, traditional "Food Microbiology" teaching remains primarily centered on theoretical lectures and basic experiments such as microbial cultivation, counting, and Gram staining. This significant disconnect between teaching content and cutting-edge industry demands constrains the cultivation of interdisciplinary talents. "Food Microbiology" grounded in microbiology, integrates advanced domains from multiple disciplines, including food science, bioinformatics, food chemistry, and fermentation engineering. This article systematically explores reform for AI-empowered undergraduate "Food Microbiology" course instruction. It advocates advancing educational reforms by restructuring course content, innovating teaching models, and enhancing practical training. By incorporating AI foundational theory and frontier case studies, an integrated online-offline teaching resource repository is constructed. Hybrid virtual-physical experimental projects are designed to strengthen students′ ability to utilize AI tools in solving practical problems like fermentation optimization and microbial identification. Industry-academia-research platforms are leveraged to establish an innovation capability cultivation mechanism. Based on a case study at Zhejiang A&F University, this article details the reform plan for AI integration into the food microbiology curriculum, demonstrating concrete applications of AI technology in enhancing instructional content such as food fermentation, metabolic prediction, and food safety early-warning systems. The integration of AI with "Food Microbiology" education will propel a transition from "experience-driven" to "data-driven" pedagogy, thereby providing sustainable talent support for the intelligent advancement of the food industry.