Artificial Intelligence (AI) is transforming the food industry by optimizing processes, improving food quality and safety, and fostering innovation. This review explores AI's applications in food science, including supply chain management, production, sensory science, and personalized nutrition. Techniques like knowledge-based expert systems, fuzzy logic, artificial neural networks, and machine learning are used for predictive maintenance, quality control, product development, and waste management. AI integrated with sensors enhances real-time monitoring and decision-making in food safety and packaging. However, challenges such as ethical concerns, data security, transparency, and high costs persist. AI can advance sustainability by optimizing resource use, enhance food security through predictive analytics of crop yields, and drive innovation in personalized nutrition and supply chain automation. The paper highlights AI's transformative potential in the food industry while addressing the obstacles to its widespread adoption. AI is being used in various areas such as supply chain management, manufacturing and production, sensory science, new product development, food quality and safety operations, processing facilities and waste management, packaging, resources conservation and energy reduction, marketing and sales, QSRs and cafetarias, and personalized nutrition. Patent trends show extensive applications of AI in food sorting, safety, nutrition, delivery, and supply chain management. However, issues such as data security, ethics, transparency, standardization, high costs, integration difficulties, and scalability remain challenges. AI adoption varies globally due to differences in infrastructure, regulations, and economic priorities. While AI presents transformative potential, its full-scale adoption is hindered by challenges such as high implementation costs, data security concerns, lack of transparency, and the complexity of integration with existing systems. Ethical considerations, including privacy and fairness in AI decision-making, further complicate the landscape. For AI to be widely embraced in the food industry, these issues must be addressed through well-defined regulations, transparency initiatives, and efforts to make AI technology more accessible to smaller players.Artificial Intelligence (AI) is transforming the food industry by optimizing processes, improving food quality and safety, and fostering innovation. This review explores AI's applications in food science, including supply chain management, production, sensory science, and personalized nutrition. Techniques like knowledge-based expert systems, fuzzy logic, artificial neural networks, and machine learning are used for predictive maintenance, quality control, product development, and waste management. AI integrated with sensors enhances real-time monitoring and decision-making in food safety and packaging. However, challenges such as ethical concerns, data security, transparency, and high costs persist. AI can advance sustainability by optimizing resource use, enhance food security through predictive analytics of crop yields, and drive innovation in personalized nutrition and supply chain automation. The paper highlights AI's transformative potential in the food industry while addressing the obstacles to its widespread adoption. AI is being used in various areas such as supply chain management, manufacturing and production, sensory science, new product development, food quality and safety operations, processing facilities and waste management, packaging, resources conservation and energy reduction, marketing and sales, QSRs and cafetarias, and personalized nutrition. Patent trends show extensive applications of AI in food sorting, safety, nutrition, delivery, and supply chain management. However, issues such as data security, ethics, transparency, standardization, high costs, integration difficulties, and scalability remain challenges. AI adoption varies globally due to differences in infrastructure, regulations, and economic priorities. While AI presents transformative potential, its full-scale adoption is hindered by challenges such as high implementation costs, data security concerns, lack of transparency, and the complexity of integration with existing systems. Ethical considerations, including privacy and fairness in AI decision-making, further complicate the landscape. For AI to be widely embraced in the food industry, these issues must be addressed through well-defined regulations, transparency initiatives, and efforts to make AI technology more accessible to smaller players.