Ongoing advancements in computational algorithms have established artificial intelligence (AI)-based models as among the most advanced technologies within the healthcare sector. AI demonstrates significant contributions to multiple medical domains, including data analysis and monitoring, imaging assessment and diagnosis, as well as therapy response and survival forecasting. Notwithstanding progress in clinical oncology, greater effort is required to customize therapy strategies according to each patient’s distinct transcriptome profile within the context of precision/personalized oncology. Moreover, the conventional analytical approach is incompatible with the thorough interpretation of substantial data streams, hence hindering the accurate prediction of treatment alternatives. In this chapter, we have reviewed how artificial intelligence utilizing big data can uncover concealed patterns, significant facts, and relevant knowledge inside vast datasets. Machine learning and deep learning, as subsets of artificial intelligence, can be employed to extract in-depth information from genomes, transcriptomics, proteomics, radiomics, digital pathological pictures, and other data, enabling doctors to synthesize and thoroughly comprehend cancer.