Harnessing Artificial Intelligence to Safeguard Food Quality and Safety

Abstract

Artificial Intelligence (AI) is reforming the food industry, particularly in food safety and quality control, by enhancing detection, predicting shelf life, and optimizing production processes. This review explores the innovative role of AI, focusing on the integration of machine learning (ML), computer vision, and natural language processing (NLP) in food safety systems. AI is transforming food safety by enabling real-time monitoring, predictive analytics, rapid contaminant detection, and automation throughout the food supply chain. These technologies reduce human error and allow quicker responses to safety threats, ultimately preventing foodborne illnesses and improving product quality. AI also helps to predict and manage climate-induced risks, such as chemical and microbiological hazards linked to extreme weather and temperature shifts. The review outlines the integration of digital tools such as biosensors and Internet of Things (IoT) devices and examines AI’s convergence with blockchain and process analytical technologies to enhance traceability and strengthen food safety management systems. Despite its potential, the widespread adoption of AI is hindered by challenges such as data privacy concerns, workforce adaptation, and regulatory barriers, while critical gaps in digital infrastructure, data standardization, and policy support also need to be addressed to enable effective implementation. The review highlights the importance of ethical frameworks and interdisciplinary collaboration to guide responsible AI deployment. Emerging tools like neural networks and behavior-based safety assessments can boost food system resilience. The review concludes by calling for enhanced regulatory cooperation and technological investment to realize AI’s full potential in creating safer, more sustainable, and efficient food systems.