Journal of Advances in Food Science & Technology

Food safety and quality remain major public health and economic concerns in developing countries such as Ghana, where microbial contamination, chemical hazards, and food fraud persist across food supply chains. The increasing complexity of modern food systems has intensified the need for innovative, efficient, and scalable monitoring approaches. Artificial intelligence (AI) has emerged as a transformative tool capable of enhancing food safety and quality control through data-driven, real-time, and non-destructive assessment techniques. This narrative critical review synthesizes existing literature on the application of AI technologies—particularly machine learning and computer vision—in addressing food safety and quality challenges in Ghana. Machine learning models are increasingly applied for contaminant detection, risk prediction, and supply chain optimization, while computer vision systems enable automated food fraud detection, quality grading, and defect identification in agricultural products. The review also examines the integration of AI with complementary technologies such as blockchain and the Internet of Things to strengthen traceability and transparency across food systems. Despite their demonstrated potential, the adoption of AI technologies in Ghana remains constrained by key challenges, including infrastructure limitations, high implementation costs, limited technical expertise, and sociocultural barriers. Future research should prioritize low-cost, scalable machine learning and computer vision solutions tailored to informal food systems and high-risk commodities in low- and middle-income countries. Overall, this review bridges the gap between technological innovation and practical implementation, offering insights relevant to strengthening food safety systems in Ghana and comparable resource-constrained settings.