eng Oriental Scientific Publishing Company Biosciences Biotechnology Research Asia 0973-1245 2026-03-30 23 1 97 116 10.13005/bbra/3483 58398 article Kotaiah Silakabattini 1 Vidyadhara Suryadevara 1 R.L.C. Sasidhar, 1 Yapuri Ashok Kumar 1 Sharabu Ravi Chandra 1 Chadalawada Aruna Kumar 1 Chebrolu Hanumaiah Institute of Pharmaceutical Sciences, Chandramoulipuram, Chowdavaram, Guntur, Andhra Pradesh, India. Diabetes mellitus is a multifactorial metabolic disease that involves multiple complex molecular interactions ranging from genetic predisposition, proteomic alteration, and metabolic disturbance. Traditional single-omics approaches have been unable to capture the systemic landscape of diseases, including the understanding of disease onset, progression, heterogeneity, and response to treatment. Recent progress in multi-omics integration with the aid of artificial intelligence (AI) and machine learning (ML) has made biomarker discovery a revolution by mapping interconnected biological networks. This review provides a synthesis of the current state of progress in genomics, transcriptomics, proteomics, and metabolomics integration using AI-driven computational frameworks for the discovery of predictive, diagnostic, and prognostic biomarkers in diabetes. We discuss analytical pipelines, tools of network biology, deep learning architectures, the issues of clinical translation, ethical concerns, and future aspects of precision diabetology. https://www.biotech-asia.org/vol23no1/ai-powered-multi-omics-analysis-for-novel-diabetes-biomarker-discovery-interlinking-metabolomic-genomic-and-proteomic-networks/ Artificial intelligence; Biomarkers; Diabetes; Machine learning; Mapping