Precision oncology in medicine treats a cancer patient based on the molecular characteristics. Traditionally, cancer therapy relied on histological classifications and site of origin, but this paradigm fails to take into account tumor heterogeneity. Using immense analytical NGS assays and/or multiomic environments and functional assays, the tumor has undergone deeper profiling for the detection of actionable biomarkers and patient-tailored therapy selection. The practice of using cohort clinical trial designs such as basket, umbrella, platform, and N-of-1 trials arose in response to the molecular complexity of cancers, aiming to fast-track the evaluation of targeted therapies. Due to the limited durability of monotherapies, drug combinations and adaptive dosing are increasingly employed. Artificial intelligence with advanced machine learning finds real-time application in sifting through enormous molecular data for drug match and trial optimization. Decentralized as well as community-engaged trials keep widening the horizon of therapeutics, especially for the needy areas, while real-world evidence, synthetic control arms, and patient-reported outcomes enrich evidence generation. Trials of I-PREDICT, WINTHER, NCI-MATCH, TAPUR, and TRACK exemplify how these approaches are applied across tumor types and settings. Ethical issues, such as informed consent, data privacy, equity in access, etc., are essential for the future of precision oncology. Clinical implementation is advancing, yet challenges persist due to resistance mechanisms, hurdles in data interpretation, and disparities in access to genomic testing and targeted therapies. However, with innovations in diagnosis and trial methods, combined with an integration of data, precision medicine promises to change cancer care—a step toward more effective and individualized treatment.