Personalized cancer therapy represents a transformative approach to oncology, tailoring treatments based on individual genetic and molecular profiles. However, its implementation faces significant clinical and technical challenges that hinder widespread adoption. Multiple technical challenges, for example, problems in biomarker validation, tumor heterogeneity, and integration of patients’ data, hinder its widespread application. The complex process of multi-omics, requirements for high-throughput sequencing, and the limitations of artificial intelligence prediction models further complicate the development of successful personalized medicines, forcing continuous adaptation of new strategies. Other challenges include, but are not limited to, accessibility barriers, high cost, and multiple regulatory constraints. The integration of biomarker-driven strategies into clinics needs robust validation protocols and new trial designs that can ensure efficacy. Likewise, immune evasion complicates responses to immunotherapy, particularly in “cold” tumors. Also, the ethical concerns regarding patients’ genomic data privacy may further slow down the viability of personalized therapy. In this chapter, we explore the technical and clinical obstacles in personalized oncology, highlighting emerging tools, for example, AI-driven biomarker discovery, patients’ molecular profiling, and multiple adaptive treatment strategies. Addressing these challenges may play a crucial role in improving outcomes and advancing the field of precision oncology.