Personalized cancer therapy is transforming oncology by tailoring treatment paradigms to patient’s unique molecular tumor profiles. This chapter focuses on using next-generation sequencing (NGS), single-cell RNA sequencing, and liquid biopsies, combined with computational biology, to tackle tumor heterogeneity and personalize precision medicine. Clinicians can define actionable mutations and develop personalized treatments by deciphering genomic, transcriptomic, proteomic, and epigenomic changes. Bioinformatics is essential to analyze complex data. The processes include variant calling, pathway analysis, neoantigen prediction, and clinical records integration, which are now integral to personalized oncology pipelines. The role of machine learning (ML) and artificial intelligence (AI) is to improve this process by determining oncogenic drivers, drug response predictions, and patient stratification. PERCEPTION and PanDrugs exemplify how therapeutic optimization surpasses guidelines using AI-derived insights. Multi-omics integration enhances resolution by correlating several molecular levels, where emergent biomarkers and resistance mechanisms are uncovered. Computational models like MOFA and netDx enable patient stratification and predictive modeling of outcomes. NCI-MATCH, WINTHER, and I-PREDICT trial case studies illustrate the clinical utility of this approach. Challenges are ongoing; however, with data standardization, interpretability of models, and access parity, there has been an enhancement in the prediction efficiency. Ethical considerations about privacy and informed consent are paramount given the growing clinical use of genomic data. Directions for future development are broadening diverse omics databases, enhancing transparency in AI, and creating predictive models like digital twins for adaptive therapy. Computer-aided personalized cancer treatments provide a paradigm shift toward effective, accurate, and patient-specific oncologic treatment.