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However, existing studies are often confined to specific applications, with limited integration across different stages of the packaging life cycle and insufficient linkage between material performance, functionality, and system\u2010level outcomes. This review systematically analyzes peer\u2010reviewed studies retrieved from the Web of Science Core Collection (2021\u20132025), selected based on their relevance to AI applications in food packaging, including material performance, safety, and life cycle management. A life cycle\u2013oriented framework is proposed, linking major AI paradigms (supervised, unsupervised, reinforcement, deep learning, and hybrid models) to six key domains: material design, production optimization, food quality prediction, safety assurance, smart labeling and traceability, and recycling. Within this framework, AI supports data\u2010driven prediction, monitoring, and decision\u2010making, whereas hybrid models improve robustness in complex, multifactor systems. Despite challenges related to data quality, model generalization, and regulatory acceptance, AI\u2010driven packaging systems may support a transition from passive containment toward more adaptive and data\u2010informed solutions that improve efficiency, sustainability, and consumer trust.<\/jats:p>","DOI":"10.1111\/1541-4337.70486","type":"journal-article","created":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T05:34:31Z","timestamp":1776922471000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Artificial Intelligence for Food Packaging: A Life Cycle\u2013Oriented Review of Material Performance, Functionality, Safety, and Sustainability"],"prefix":"10.1111","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1472-4563","authenticated-orcid":false,"given":"Kehao","family":"Huang","sequence":"first","affiliation":[{"name":"School of Light Industry and Engineering South China University of Technology  Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenhao","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Light Industry and Engineering South China University of Technology  Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yixiang","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Food Science and Agricultural Chemistry McGill University  Sainte Anne de Bellevue Quebec Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoying","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Light Industry and Engineering South China University of Technology  Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,4,22]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1080\/87559129.2025.2585134"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.3390\/foods12203811"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfca.2021.103920"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tifs.2025.105153"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3135973"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12596-021-00772-1"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.3390\/jimaging7090186"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00216-024-05632-y"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40808-024-02201-1"},{"issue":"2","key":"e_1_2_11_11_1","first-page":"1","article-title":"Digital Driven Real\u2010Time Logistics Impact on Global Supply Chain Resilience: An Empirical Study","volume":"1","author":"Bhilat E. 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