{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T14:19:58Z","timestamp":1789395598651,"version":"build-2803163510"},"reference-count":67,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T00:00:00Z","timestamp":1789344000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Brain Sci.","Brain Sciences"],"abstract":"<jats:p>Background\/Objectives: The accelerating integration of machine learning (ML) with molecular, imaging, and physiological data is transforming Alzheimer\u2019s disease (AD) research. Methods &amp; Results: Recent studies demonstrate that multimodal, AI-assisted platforms can enhance early diagnosis, predict biomarker trajectories, and identify novel therapeutic targets. This mini-review covers the evolving AD diagnostic and biomarker frameworks, current therapeutic strategies including recently approved anti-amyloid immunotherapies, and advances from contemporary studies employing ML across diverse data streams, ranging from cerebrospinal fluid (CSF) and plasma proteomics to Raman spectroscopy, neuroimaging, transcriptomics, and microbiome signatures. Conclusions: Collectively, they illustrate how artificial intelligence (AI) has shifted A biomarker discovery from univariate to network-based inference, achieving clinically relevant accuracy while emphasizing model interpretability. We discuss biological insights, translational implications, and persisting challenges related to validation, bias, and regulatory integration.<\/jats:p>","DOI":"10.3390\/brainsci16090969","type":"journal-article","created":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T13:05:26Z","timestamp":1789391126000},"page":"969","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning and Multimodal Biomarker Discovery in Alzheimer\u2019s Disease"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-2738-5181","authenticated-orcid":false,"given":"Tariq","family":"Tayebi","sequence":"first","affiliation":[{"name":"Normanhurst Boys High School, Normanhurst, NSW 2076, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Monique A.","family":"David","sequence":"additional","affiliation":[{"name":"School of Medicine, Blacktown\/Mt Druitt Clinical School, Western Sydney University, Blacktown, NSW 2148, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8664-6918","authenticated-orcid":false,"given":"Mourad","family":"Tayebi","sequence":"additional","affiliation":[{"name":"School of Medicine, Blacktown\/Mt Druitt Clinical School, Western Sydney University, Blacktown, NSW 2148, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e14528","DOI":"10.1002\/alz.14528","article-title":"Considerations in the clinical use of amyloid PET and CSF biomarkers for Alzheimer\u2019s disease","volume":"21","author":"Leuzy","year":"2025","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Vrahatis, A.G., Skolariki, K., Krokidis, M.G., Lazaros, K., Exarchos, T.P., and Vlamos, P. 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