This review explores the potential of artificial intelligence (AI) in cancer diagnosis and prognosis, analyzing 89 recent studies from 2020 to 2023. The studies focus on AI applications in multi-omics data, including radiomics, pathomics, clinical records, and lab data. Eight studies integrated diverse omics data types such as genomics, transcriptomics, epigenomics, and proteomics. AI integration with clinical and omics data significantly advances cancer diagnosis and prognosis, essential for safe clinical implementation. The review categorizes studies into eight sections based on data types, with subsections on diagnosis and prognosis. Key AI models include Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), XGBoost, and Convolutional Neural Networks (CNN). These models are used for tasks like tumor classification, gene expression prediction, and survival analysis. The study highlights the importance of AI in improving diagnostic accuracy, personalizing treatment, and enhancing operational efficiency. Challenges include data heterogeneity, model interpretability, and ethical considerations. The review emphasizes the need for standardized data and robust prospective studies to ensure AI safety and efficacy in clinical settings.This review explores the potential of artificial intelligence (AI) in cancer diagnosis and prognosis, analyzing 89 recent studies from 2020 to 2023. The studies focus on AI applications in multi-omics data, including radiomics, pathomics, clinical records, and lab data. Eight studies integrated diverse omics data types such as genomics, transcriptomics, epigenomics, and proteomics. AI integration with clinical and omics data significantly advances cancer diagnosis and prognosis, essential for safe clinical implementation. The review categorizes studies into eight sections based on data types, with subsections on diagnosis and prognosis. Key AI models include Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), XGBoost, and Convolutional Neural Networks (CNN). These models are used for tasks like tumor classification, gene expression prediction, and survival analysis. The study highlights the importance of AI in improving diagnostic accuracy, personalizing treatment, and enhancing operational efficiency. Challenges include data heterogeneity, model interpretability, and ethical considerations. The review emphasizes the need for standardized data and robust prospective studies to ensure AI safety and efficacy in clinical settings.