Introduction: Artificial Intelligence (AI) technologies are revolutionizing the field of food safety assessment and monitoring by offering advanced tools for the prediction, detection, and management of food-borne hazards. This paper explores the combating of food fraud and adulteration using Artificial Intelligence. Food fraud and adulteration are critical issues that threaten public health, consumer trust, and the integrity of global food supply chains. Despite regulatory efforts, traditional methods for detecting and preventing food fraud are often insufficient due to their reliance on manual inspections and limited testing capabilities. The complexity and scale of modern food supply chains require more advanced solutions. These illicit practices involve the intentional misrepresentation or alteration of food products for economic gain, including mislabeling, dilution, and contamination. This abstract explores the role of Artificial Intelligence (AI) in addressing these issues, focusing on detection, traceability, and prevention strategies.

Methods: AI-based methods such as machine learning, deep learning, natural language processing and block chain integration are analyzed for their ability to enhance various aspects of food safety. AI technologies, particularly machine learning and deep learning, offer new capabilities for identifying and mitigating food fraud.

Results: These AI methods can significantly enhance the detection, prevention, and management of food fraud and adulteration by providing more accurate, efficient, and scalable solutions than traditional methods.

Conclusions: In conclusion, AI holds tremendous potential to revolutionize food safety by improving quality control, risk assessment, traceability, monitoring, personalized nutrition, and regulatory compliance across the food supply chain. By leveraging the power of AI, food manufacturers, regulators, and consumers can work together to ensure a safer and more secure food system for all.