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Despite major advances in high-throughput genomics, proteomics, and multimodal imaging, a substantial gap persists between molecular tumor characterization and clinically actionable therapeutic decision-making, partly due to the limitations of conventional preclinical models in capturing tumor heterogeneity and predicting patient-specific drug response. Patient-derived organoids (PDO) have emerged as a promising platform to bridge this gap by enabling functional interrogation of individual tumors in a physiologically relevant three-dimensional context. PDO retain the genomic, transcriptomic, and histopathological features of their parental tumors while supporting long-term expansion, biobanking, and high-throughput pharmacological testing. In this review, we provide a clinically oriented overview of PDO technology as a key tool in functional precision oncology, summarizing current methodologies for tissue processing, organoid derivation, and quality control. We examine applications across multiple cancer types, including drug screening, radiotherapy response modeling, immuno-oncology co-culture systems, and CRISPR-based functional genomics, highlighting their role in directly measuring therapeutic vulnerability. We also integrate tumor-specific evidence across major malignancies, including colorectal, pancreatic, and breast cancers, where PDO-based pharmacotyping shows strong concordance with clinical outcomes and is increasingly incorporated into prospective trials. Finally, we discuss the integration of PDO with emerging technologies, including organoid-on-chip systems, artificial intelligence-driven analytics, and hospital-integrated workflows, as a critical innovation layer that is redefining their clinical applicability. These integrative approaches move PDO beyond static ex vivo models toward dynamic, and decision-support systems, with the potential to substantially enhance predictive accuracy and real-time therapeutic stratification. Collectively, these advances position PDOs as a promising component in next-generation precision oncology, supporting a transition from static genomics-based stratification toward dynamic, functionally guided therapeutic decision-making.