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 for analyzing multi-omics data, including radiomics, pathomics, clinical records, and lab data. Notably, eight studies combined diverse omics data types (genomics, transcriptomics, epigenomics, and proteomics). The integration of AI with clinical and omics data is crucial for advancing cancer diagnosis and prognosis, contributing to safe clinical implementation. The review categorizes the studies into eight sections based on the type of data used and further subdivides them into cancer diagnosis and prognosis subsections. The Random Forest (RF) method is the most commonly used, while Convolutional Neural Networks (CNNs) are prominent in radiomics and pathomics data analysis. The review highlights the importance of interdisciplinary collaboration and ethical considerations in the development and application of AI in oncology. Despite the promising outcomes, challenges remain in achieving feature reproducibility and model interpretability, necessitating robust prospective studies to ensure the safety and efficacy of AI models.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 for analyzing multi-omics data, including radiomics, pathomics, clinical records, and lab data. Notably, eight studies combined diverse omics data types (genomics, transcriptomics, epigenomics, and proteomics). The integration of AI with clinical and omics data is crucial for advancing cancer diagnosis and prognosis, contributing to safe clinical implementation. The review categorizes the studies into eight sections based on the type of data used and further subdivides them into cancer diagnosis and prognosis subsections. The Random Forest (RF) method is the most commonly used, while Convolutional Neural Networks (CNNs) are prominent in radiomics and pathomics data analysis. The review highlights the importance of interdisciplinary collaboration and ethical considerations in the development and application of AI in oncology. Despite the promising outcomes, challenges remain in achieving feature reproducibility and model interpretability, necessitating robust prospective studies to ensure the safety and efficacy of AI models.