Immunotherapy has transformed cancer treatment, but not all patients respond, necessitating a deeper understanding of tumor biology and the tumor immune microenvironment (TIME). Multi-omics approaches, integrating genomics, transcriptomics, proteomics, metabolomics, radiomics, and immunomics, offer insights into the molecular landscape of cancer and tumor-immune interactions. These methods help identify biomarkers for patient stratification and personalized treatment. This review explores multi-omics models and their potential to change clinical practice. The TIME is crucial for immunotherapy response, categorized into immune-inflamed, immune-excluded, and immune-desert based on TILs. Multi-omics tools like ESTIMATE, xCell, and CIBERSORT analyze immune cell composition in the TIME. Computational tools such as IOBR and TIDE predict immunotherapy responses by analyzing gene signatures and tumor microenvironment features. Studies show that TIDE scores correlate with migrasome expression and immunotherapy response, while TMEscores predict response to immunotherapy in gastric cancer patients. Biomarkers like PD-L1 expression, Tumor Mutation Burden (TMB), and Tumor Infiltrating Lymphocytes (TILs) are critical for predicting immunotherapy outcomes. However, their accuracy is limited by variability in detection methods. New biomarkers, such as SERPINE1, are emerging as potential predictors. AI and multi-omics data are increasingly used to predict immunotherapy responses, with tools like CAMOIP and deep learning models improving accuracy. Clinical trials highlight the importance of TMB, MSI-H, and TILs in predicting immunotherapy responses. For example, high TMB and MSI-H status are associated with better responses to PD-1 inhibitors. However, the CPS cut-off for predicting response remains debated. MSI-H/dMMR gastric cancer patients with low TMB are less responsive to anti-PD-1 agents, emphasizing the need for combined biomarker assessments. AI and multi-omics approaches are transforming cancer immunotherapy by enabling personalized treatment strategies. These methods help identify predictive biomarkers and guide treatment decisions. Despite challenges in standardization and data integration, multi-omics and AI are advancing the field, offering new insights into tumor biology and immunotherapy response. Future research should focus on integrating diverse data sources and developing robust, standardized protocols to improve clinical outcomes.Immunotherapy has transformed cancer treatment, but not all patients respond, necessitating a deeper understanding of tumor biology and the tumor immune microenvironment (TIME). Multi-omics approaches, integrating genomics, transcriptomics, proteomics, metabolomics, radiomics, and immunomics, offer insights into the molecular landscape of cancer and tumor-immune interactions. These methods help identify biomarkers for patient stratification and personalized treatment. This review explores multi-omics models and their potential to change clinical practice. The TIME is crucial for immunotherapy response, categorized into immune-inflamed, immune-excluded, and immune-desert based on TILs. Multi-omics tools like ESTIMATE, xCell, and CIBERSORT analyze immune cell composition in the TIME. Computational tools such as IOBR and TIDE predict immunotherapy responses by analyzing gene signatures and tumor microenvironment features. Studies show that TIDE scores correlate with migrasome expression and immunotherapy response, while TMEscores predict response to immunotherapy in gastric cancer patients. Biomarkers like PD-L1 expression, Tumor Mutation Burden (TMB), and Tumor Infiltrating Lymphocytes (TILs) are critical for predicting immunotherapy outcomes. However, their accuracy is limited by variability in detection methods. New biomarkers, such as SERPINE1, are emerging as potential predictors. AI and multi-omics data are increasingly used to predict immunotherapy responses, with tools like CAMOIP and deep learning models improving accuracy. Clinical trials highlight the importance of TMB, MSI-H, and TILs in predicting immunotherapy responses. For example, high TMB and MSI-H status are associated with better responses to PD-1 inhibitors. However, the CPS cut-off for predicting response remains debated. MSI-H/dMMR gastric cancer patients with low TMB are less responsive to anti-PD-1 agents, emphasizing the need for combined biomarker assessments. AI and multi-omics approaches are transforming cancer immunotherapy by enabling personalized treatment strategies. These methods help identify predictive biomarkers and guide treatment decisions. Despite challenges in standardization and data integration, multi-omics and AI are advancing the field, offering new insights into tumor biology and immunotherapy response. Future research should focus on integrating diverse data sources and developing robust, standardized protocols to improve clinical outcomes.