Personalized medicine adapts clinical decisions to the individual rather than to the population average, drawing on the molecular, genetic, environmental, and lifestyle features of each patient when tests and treatments are chosen. Where conventional protocols apply a single regimen across heterogeneous groups, individualised approaches identify the subset of patients most likely to benefit from a given intervention and at what dose. The supporting toolkit includes high-throughput sequencing, proteomic profiling, pharmacogenomic assays, biomarker measurement, and computational methods drawn from machine learning and deep learning that integrate clinical and molecular records. Such methods are used to stratify risk, predict therapeutic response, and shorten time to diagnosis, with established applications in oncology, rare inherited disease, and pharmacogenomically guided prescribing. Reported gains include closer alignment of drug to patient, fewer avoidable adverse reactions, and more efficient use of clinical resources along the care pathway. Active patient participation is encouraged, since care plans can be tuned to the individual's situation and treatment goals. Several barriers continue to slow broad uptake, among them the unit cost of sequencing and targeted agents, the protection of identifiable genetic information, regulatory variation across jurisdictions, and shortages of clinical infrastructure and trained personnel. As sequencing prices fall and clinical informatics matures, personalized medicine is projected to extend beyond current strongholds into more areas of routine practice, supporting more accurate, patient-centred, and economically sustainable care.