This paper explores the transformative potential of artificial intelligence (AI) and machine learning (ML) in addressing food integrity challenges, particularly in enhancing food safety and quality. AI and ML, along with other Industry 4.0 technologies, have been increasingly applied in the food industry to improve supply chain resilience and public health. The paper reviews the current advancements and applications of these technologies in food integrity, focusing on quality control, food fraud detection, process control, risk assessments, prediction, and supply chain traceability. Key applications include computer vision for quality inspection, hyperspectral imaging for compositional analysis, blockchain for traceability, and real-time monitoring systems. The integration of AI and ML has led to improved food integrity standards, enhanced public health, and increased consumer trust. However, the paper also acknowledges the challenges associated with domain-specific implementation and emphasizes the need to overcome these obstacles to fully harness the capabilities of AI and ML in safeguarding the food system.This paper explores the transformative potential of artificial intelligence (AI) and machine learning (ML) in addressing food integrity challenges, particularly in enhancing food safety and quality. AI and ML, along with other Industry 4.0 technologies, have been increasingly applied in the food industry to improve supply chain resilience and public health. The paper reviews the current advancements and applications of these technologies in food integrity, focusing on quality control, food fraud detection, process control, risk assessments, prediction, and supply chain traceability. Key applications include computer vision for quality inspection, hyperspectral imaging for compositional analysis, blockchain for traceability, and real-time monitoring systems. The integration of AI and ML has led to improved food integrity standards, enhanced public health, and increased consumer trust. However, the paper also acknowledges the challenges associated with domain-specific implementation and emphasizes the need to overcome these obstacles to fully harness the capabilities of AI and ML in safeguarding the food system.