<![CDATA[The recent rapid evolution of artificial intelligence (AI), big data analytics, and multi-omics technologies is changing modern precision oncology. These tools have opened up new opportunities to understand the heterogeneity of the tumor, drug response, and biomarker discovery. Traditional cancer therapies often fail because we do not fully understand the genomic, transcriptomic, proteomic, and metabolomic differences that are present between patients and within tumor microenvironments. Recent progress in computational intelligence, integrative omics pipelines, and drug discovery through machine learning holds significant potential to enable the personalization of cancer treatment, identify new anticancer compounds, and accelerate the development of new therapeutics. This study provides a detailed analysis of how AI-enabled data analytics and the integration of multi-omics capabilities are transforming next-generation precision oncology and the development of anticancer drugs. It synthesizes the insights from the recent studies such as big data facilitated plant biotechnology for bioactive anticancer compounds (Ahmed et al., 2023), machine learning enabled genomic selection framework (Saimon et al., 2023), artificial intelligence based on ischemic stroke biomarker discovery (Manik, 2023), cervical cancer prediction (Manik, 2022), predictive multi-omics system of neurodegenerative disease (Manik, 2021), and chronic disease analytics (Manik et al., 2021) to describe the potential of innovative computational frameworks to overcome existing Generative AI, deep learning, hybrid ML, and systems biology stand out as pillars on precision drug discovery, immuno-oncology improvement, high throughput compound selection, and early diagnosis of various cancers. The paper then develops a conceptual AI-driven multi-omics architecture for real-world oncology applications. It demonstrates how the genomic layer, transcript sequencing layer, epigenomic layer, proteome, microbiomics, and metabolomics layers can be harmonized using machine learning, federated learning, Bayesian optimization, and network-based models. By addressing literature from both modern times and fundamentals, this work uncovers gaps in the current oncology pipelines, suggests new strategies in AI for real-world translation into clinical oncology, and thereby establishes the potential of bioinformatics-driven solutions in anticancer drug development. The results highlight the importance of interdisciplinary research and data science approaches in providing equitable, individualized, and high-precision cancer care.]]>
