{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T11:06:20Z","timestamp":1789988780296,"version":"4.0.1"},"reference-count":122,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T00:00:00Z","timestamp":1789776000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Microorganisms"],"abstract":"<jats:p>The microbiome, as a vast and dynamic community of microbes, is now recognized as a key regulator of host physiology, profoundly influencing health and susceptibility to disease. Accordingly, probiotics are a mainstay of prevention and treatment. But the individual complexity and dynamic specificity of an individual\u2019s microbiome make the previous \u201cone-size-fits-all\u201d research model completely invalid. This review systematically analyzes the applications of artificial intelligence (AI) and machine learning (ML) as transformative computational tools necessary to surmount these challenges. In this article, we detail how these computational methods have been applied throughout the entire research and development pathway of probiotics, including novel strain identification (through multi-omics analysis), formulation and production optimization, and elucidation of complex mechanisms of action in the host. Furthermore, we highlight the emerging frontier of personalized probiotic therapy, demonstrating how AI\/ML can be utilized to predict treatment efficacy based on individual host data. The objective of this article is to provide a detailed discourse on the actual and prospective applications of AI and ML in this process, ultimately delineating their revolutionary potential to inform the design of the next generation of probiotics with unprecedented precision, efficacy, and sustainability.<\/jats:p>","DOI":"10.3390\/microorganisms14092103","type":"journal-article","created":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T10:25:43Z","timestamp":1789986343000},"page":"2103","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The Role of Artificial Intelligence and Machine Learning in Revolutionizing Probiotic Research"],"prefix":"10.3390","volume":"14","author":[{"given":"Reza","family":"Nori","sequence":"first","affiliation":[{"name":"Department of Systems Biotechnology, Institute of Industrial and Environmental Biotechnology, National Institute of Genetic Engineering and Biotechnology, Tehran P.O. Box 14965\/161, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Parvin","family":"Shariati","sequence":"additional","affiliation":[{"name":"Department of Bioprocess Engineering, Institute of Industrial and Environmental Biotechnology, National Institute of Genetic Engineering and Biotechnology, Tehran P.O. Box 14965\/161, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Weiland-Br\u00e4uer, N. (2021). Friends or foes\u2014Microbial interactions in nature. Biology, 10.","DOI":"10.3390\/biology10060496"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Shawky, E., Surendran, S., and El-Khair, R.M.A. (2025). Fermented Vegetables as a Source of Psychobiotics: A Review of the Evidence for Mental Health Benefits. Probiotics Antimicrob. 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