The article discusses the application of graph machine learning in integrated multi-omics analysis, emphasizing its potential to enhance the understanding of complex biological systems and improve clinical outcomes. Multi-omics experiments, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and metagenomics, provide comprehensive insights into cellular and microenvironmental processes. However, integrating these diverse datasets poses challenges due to data heterogeneity, noise, and missing information. Graph-based approaches, such as graph neural networks (GNNs), offer a robust framework for integrating and analyzing multi-omics data by capturing complex relationships and interactions among different molecular entities. GNNs can handle large and heterogeneous datasets, provide interpretable model predictions, and leverage prior network-based knowledge. The article highlights several research articles that demonstrate the effectiveness of graph-based methods in various applications, including patient classification, biomarker identification, and disease subtyping. Despite the challenges, graph machine learning continues to push the boundaries of technological capabilities, offering valuable insights into precision medicine and personalized therapy.The article discusses the application of graph machine learning in integrated multi-omics analysis, emphasizing its potential to enhance the understanding of complex biological systems and improve clinical outcomes. Multi-omics experiments, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and metagenomics, provide comprehensive insights into cellular and microenvironmental processes. However, integrating these diverse datasets poses challenges due to data heterogeneity, noise, and missing information. Graph-based approaches, such as graph neural networks (GNNs), offer a robust framework for integrating and analyzing multi-omics data by capturing complex relationships and interactions among different molecular entities. GNNs can handle large and heterogeneous datasets, provide interpretable model predictions, and leverage prior network-based knowledge. The article highlights several research articles that demonstrate the effectiveness of graph-based methods in various applications, including patient classification, biomarker identification, and disease subtyping. Despite the challenges, graph machine learning continues to push the boundaries of technological capabilities, offering valuable insights into precision medicine and personalized therapy.