Graph machine learning offers a promising approach for integrated multi-omics analysis in biomedical research. Multi-omics data, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, provide comprehensive insights into biological processes and disease mechanisms. Traditional data integration methods have limitations, but graph-based approaches, such as graph neural networks, can model complex relationships and interactions among different omics datasets. These methods can incorporate prior biological knowledge, such as protein-protein interaction networks, to enhance predictive accuracy and interpretability. Graph-based workflows enable the integration of multi-omics data by representing it as a graph, where nodes represent biological entities and edges represent relationships. This approach allows for the discovery of patterns and the modeling of complex interactions, which is crucial for understanding disease mechanisms and identifying biomarkers. Various graph machine learning techniques, including graph autoencoders, variational autoencoders, and graph convolutional networks, have been applied to multi-omics data to improve classification, clustering, and prediction tasks. Several studies have demonstrated the effectiveness of graph-based methods in multi-omics analysis. For example, graph-based approaches have been used to classify cancer subtypes, predict patient survival, and identify potential biomarkers. These methods often outperform traditional machine learning techniques in terms of accuracy and interpretability. Additionally, integrating multi-omics data with biological networks has shown promise in improving the generalizability and robustness of models. Despite these advancements, challenges remain in the application of graph machine learning to multi-omics data. These include handling data heterogeneity, ensuring model interpretability, and addressing computational complexity. However, ongoing research is exploring ways to overcome these challenges, such as using self-supervised learning and contrastive learning to reduce reliance on labeled data. The integration of graph-based methods with multi-omics data is expected to continue advancing precision medicine and personalized therapeutic strategies.Graph machine learning offers a promising approach for integrated multi-omics analysis in biomedical research. Multi-omics data, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, provide comprehensive insights into biological processes and disease mechanisms. Traditional data integration methods have limitations, but graph-based approaches, such as graph neural networks, can model complex relationships and interactions among different omics datasets. These methods can incorporate prior biological knowledge, such as protein-protein interaction networks, to enhance predictive accuracy and interpretability. Graph-based workflows enable the integration of multi-omics data by representing it as a graph, where nodes represent biological entities and edges represent relationships. This approach allows for the discovery of patterns and the modeling of complex interactions, which is crucial for understanding disease mechanisms and identifying biomarkers. Various graph machine learning techniques, including graph autoencoders, variational autoencoders, and graph convolutional networks, have been applied to multi-omics data to improve classification, clustering, and prediction tasks. Several studies have demonstrated the effectiveness of graph-based methods in multi-omics analysis. For example, graph-based approaches have been used to classify cancer subtypes, predict patient survival, and identify potential biomarkers. These methods often outperform traditional machine learning techniques in terms of accuracy and interpretability. Additionally, integrating multi-omics data with biological networks has shown promise in improving the generalizability and robustness of models. Despite these advancements, challenges remain in the application of graph machine learning to multi-omics data. These include handling data heterogeneity, ensuring model interpretability, and addressing computational complexity. However, ongoing research is exploring ways to overcome these challenges, such as using self-supervised learning and contrastive learning to reduce reliance on labeled data. The integration of graph-based methods with multi-omics data is expected to continue advancing precision medicine and personalized therapeutic strategies.