This review explores the transformative potential of artificial intelligence (AI) and machine learning (ML) in enhancing food integrity, focusing on their applications in food safety, quality control, supply chain traceability, risk assessment, and pathogen detection. AI and ML, alongside technologies like big data, blockchain, and IoT, are increasingly used in the food industry to improve food safety and quality, ensure traceability, and enhance the resilience of the food supply chain. The paper highlights key areas where AI and ML are applied, including quality control, food fraud detection, process control, risk assessment, prediction, and supply chain traceability. These technologies have significantly improved food integrity standards, public health, and consumer trust. However, challenges such as domain-specific implementation and data privacy concerns remain. The paper also discusses the potential of AI and ML in predictive analytics for food safety, real-time monitoring, and pathogen detection, emphasizing their role in identifying and mitigating foodborne hazards. Overall, AI and ML are seen as critical tools for addressing complex food integrity challenges and ensuring a safer, more transparent food system.This review explores the transformative potential of artificial intelligence (AI) and machine learning (ML) in enhancing food integrity, focusing on their applications in food safety, quality control, supply chain traceability, risk assessment, and pathogen detection. AI and ML, alongside technologies like big data, blockchain, and IoT, are increasingly used in the food industry to improve food safety and quality, ensure traceability, and enhance the resilience of the food supply chain. The paper highlights key areas where AI and ML are applied, including quality control, food fraud detection, process control, risk assessment, prediction, and supply chain traceability. These technologies have significantly improved food integrity standards, public health, and consumer trust. However, challenges such as domain-specific implementation and data privacy concerns remain. The paper also discusses the potential of AI and ML in predictive analytics for food safety, real-time monitoring, and pathogen detection, emphasizing their role in identifying and mitigating foodborne hazards. Overall, AI and ML are seen as critical tools for addressing complex food integrity challenges and ensuring a safer, more transparent food system.