Asian Journal of Advances in Research

Artificial intelligence (AI) is reshaping the global food industry across every stage of the value chain, from primary production to consumption. This review critically synthesises recent peer-reviewed evidence on the deployment of machine learning, deep learning, computer vision, robotics and generative AI in food safety and quality assurance, processing and manufacturing, supply chain management, primary production, and consumer-facing innovation such as personalised nutrition and flavour design. Across these domains, AI has produced measurable gains in hazard detection, process optimisation, traceability, waste reduction and product development efficiency, while also exposing persistent limitations related to data quality, model interpretability, infrastructural inequality and consumer trust. The review further examines the economic, workforce and ethical dimensions of AI adoption, highlighting tensions between productivity gains and concerns over labour displacement, data governance and algorithmic accountability. Methodologically, the review adopts a narrative rather than systematic approach, given the breadth, heterogeneity and rapid evolution of the literature, and draws on a structured multi-database search of peer-reviewed sources published between 2018 and February 2026. The synthesis suggests that AI is best understood not as a single transformative technology but as a heterogeneous family of tools whose value depends heavily on data infrastructure, regulatory clarity and organisational readiness. The review concludes that realising the full potential of AI in the food sector will require coordinated investment in interoperable data systems, harmonised governance frameworks and closer collaboration between food scientists, engineers, ethicists and policymakers.