ABSTRACT The integration of artificial intelligence (AI) into food packaging systems is transforming traditional packaging from passive containment into an intelligent, adaptive component of food quality management. This review provides a critical and systematic evaluation of AI‐driven packaging technologies published between 2015 and 2025, retrieved from Scopus, Web of Science, ScienceDirect, and IEEE Xplore databases using defined keywords related to AI, machine learning, and smart packaging. It examines how algorithms such as convolutional neural networks, recurrent neural networks, and support vector machines are integrated with embedded sensors, computer vision, and edge analytics to enable real‐time spoilage prediction, microbial risk detection, and supply‐chain traceability. The analysis incorporates model performance indicators precision, recall, and coefficient of determination ( R 2 ) and assesses limitations associated with dataset imbalance, energy consumption, and model transferability. Regulatory frameworks from European Food Safety Authority (EFSA), Food and Drug Administration (FDA), Food Safety and Standards Authority of India (FSSAI), and Codex Alimentarius are reviewed alongside sustainability aspects concerning life‐cycle impacts, e‐waste, and biodegradable AI components. Industrial implementations, including SpoilerAlert, Aryballe, and Tetra Pak's connected systems, demonstrate the transition toward commercial adoption. The review concludes by defining future directions for developing scalable, ethical, and resource‐efficient AI‐enabled packaging systems aligned with global food safety and circular economy objectives.
Bogala Madhu (2025) studied this question.