Purpose of Review <p>This review highlights the urgent need to integrate artificial intelligence (AI) into the food processing sector to meet rising global food demand, address resource constraints, and enhance safety. It outlines key AI applications to promote sustainable, efficient, and data-driven innovations, helping researchers, industry stakeholders, and policymakers leverage AI’s transformative potential to build smarter, safer, and future-ready food systems.</p> Recent Findings <p>AI and machine learning (ML) are reshaping traditional food operations, such as safety, quality assurance, and logistics. AI enhances quality control by automating defect detection, minimising human error, and ensuring compliance through real-time data and predictive maintenance. ML algorithms fine-tune processing parameters, such as temperature and ingredient ratios, to improve efficiency, minimise waste, and ensure consistency.</p> <p>AI models such as convolutional neural networks (CNNs) and support vector machines (SVMs) detect contaminants and adulterants using hyperspectral imaging and laser-induced breakdown spectroscopy. Electronic Nose systems replicate human smell to identify spoilage and residues in real time. AI-driven safety solutions use biosensors and imaging to detect microbial, chemical, and physical hazards.</p> <p>Personalised nutrition leverages AI to offer diet recommendations based on health data. In supply chains, AI supports predictive analytics, robotics, and blockchain for traceability and reduced food loss. Computer vision improves packaging and stock monitoring, while AI-based simulations and consumer preference tools accelerate new product development.</p> Summary <p>AI technologies are rapidly transforming the food industry by increasing precision, safety, and adaptability. Their integration offers promising solutions to modern challenges and paves the way for intelligent, resilient food systems.</p>