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Machine learning (ML) has emerged as a powerful computational approach for extracting biologically meaningful information from these data, enabling quantitative characterization of SC behavior and predictive modeling across diverse experimental and translational applications. This review presents a unified framework for ML in SC research by organizing the literature according to SC data sources and representations, ML methodologies and model families, and biological and clinical application domains. We review ML applications spanning SC reprogramming, differentiation, postdifferentiation maturation, quality control, disease modeling, therapeutic development, and clinical decision support, highlighting how diverse experimental data, including molecular and cellular profiling, biomedical imaging, biochemical profiling, functional measurements, and clinical data, enable biological interpretation, predictive modeling, and translational decision-making. We further discuss benchmark dataset selection, validation strategies, transformer-based foundation models, and emerging multimodal learning approaches and propose benchmark dataset selection criteria emphasizing biological diversity, experimental rigor, metadata completeness, reproducibility, and public accessibility. Collectively, this review provides a comprehensive framework for understanding current ML applications in SC research and outlines future directions toward robust, interpretable, and clinically translatable computational frameworks.<\/jats:p>","DOI":"10.34133\/csbj.0212","type":"journal-article","created":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T21:02:03Z","timestamp":1786654923000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark_01","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning in Stem Cell Research: From Biological Data to Clinical Translation"],"prefix":"10.34133","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-4165-5026","authenticated-orcid":false,"given":"Anna","family":"Nicolaou","sequence":"first","affiliation":[{"name":"Department of Computer Science, \rNew York Institute of Technology, Old Westbury, NY 11568, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2568-9078","authenticated-orcid":false,"given":"Yan","family":"Hong","sequence":"additional","affiliation":[{"name":"Department of Computer Science, \rNew York Institute of Technology, Old Westbury, NY 11568, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4427-0008","authenticated-orcid":false,"given":"Delong","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Cell Biology &amp; Molecular Medicine, \rRutgers New Jersey Medical School, Newark, NJ 07101, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7949-5549","authenticated-orcid":false,"given":"Geovanni","family":"Geukgeuzian","sequence":"additional","affiliation":[{"name":"Department of Cell Biology &amp; Molecular Medicine, \rRutgers New Jersey Medical School, Newark, NJ 07101, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7501-9564","authenticated-orcid":false,"given":"Vasisht","family":"Yegneshwaran","sequence":"additional","affiliation":[{"name":"Department of Cell Biology &amp; Molecular Medicine, \rRutgers New Jersey Medical School, Newark, NJ 07101, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3922-8104","authenticated-orcid":false,"given":"Nehal Kamal","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Cell Biology &amp; Molecular Medicine, \rRutgers New Jersey Medical School, Newark, NJ 07101, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5369-4446","authenticated-orcid":false,"given":"Diego","family":"Fraidenraich","sequence":"additional","affiliation":[{"name":"Department of Cell Biology &amp; Molecular Medicine, \rRutgers New Jersey Medical School, Newark, NJ 07101, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3677-5946","authenticated-orcid":true,"given":"Xueqing","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, \rNew York Institute of Technology, Old Westbury, NY 11568, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"221","published-online":{"date-parts":[[2026,9,30]]},"reference":[{"issue":"3","key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"52","DOI":"10.21037\/atm-23-1937","article-title":"A literature review: Machine learning-based stem cell investigation","volume":"12","author":"Ara J","year":"2024","unstructured":"Ara J, Khatun T. 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