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This review investigates and summarizes evidence on multimodal artificial intelligence that fuses such heterogeneous data to detect hazards, verify authenticity, and predict freshness within seconds. We outline sensing coverage along the chain, typical response times, and reported limits of detection, then detail data engineering practices that make disparate streams analysis\u2010ready, including time synchronization, co\u2010registration to ground truth, and robust sampling for multisite and multiseason generalization. We appraise fusion strategies, from early and late schemes to attention\u2010based hybrids that learn joint embeddings across images, spectra, and gas sensor time series, and we summarize head\u2010to\u2010head studies where multimodality improves accuracy or reduces error against unimodal baselines. We discuss the maturation of foundation scale encoders and vision language systems for food tasks, together with efficient adaptation, knowledge infusion from HACCP, and bias control. Finally, we examine edge deployment and validation in industrial settings, including hardware constraints, latency budgets, repeatability and reproducibility, documentation for audits, and perspectives on regulatory alignment in EU and US contexts, extended to China's standards\u2010driven framework where the National Health Commission (NHC) and the State Administration for Market Regulation (SAMR) jointly issue and update National Food Safety Standards (GB) that govern key compliance requirements for labelling and contaminant limits. Evidence gaps persist, notably few multisite deployments over long durations, limited public benchmarks for hyperspectral and e\u2010nose fusion, and sparse cost\u2013benefit analyses in the scholarly record. Addressing these gaps will enable trustworthy, auditable multimodal AI that complements existing controls and reduces waste while protecting consumers.<\/jats:p>","DOI":"10.1002\/fsn3.71534","type":"journal-article","created":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T04:32:11Z","timestamp":1771561931000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Multimodal\n                    <scp>AI<\/scp>\n                    for Real\u2010Time Food Safety and Quality: From Sensors to Foundation Models, Edge Deployment, and Regulation"],"prefix":"10.1002","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-0962-3068","authenticated-orcid":false,"given":"Zhaojie","family":"Chen","sequence":"first","affiliation":[{"name":"Guangzhou College of Technology and Business  Guangzhou People's Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"The Affiliated High School of South China Normal University International Department  Guangzhou People's Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fan","family":"Zhang","sequence":"additional","affiliation":[{"name":"STS Sugar Company Limited  Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,2,19]]},"reference":[{"key":"e_1_2_15_2_1","doi-asserted-by":"crossref","unstructured":"2019.\u201cEuropean Commission. 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