Pharmacogenomics is a fast-growing area that combines genomics and pharmacology to create individualized treatments based on a person’s complete genetic composition. However, current challenges involve the restricted incorporation of pharmacogenomic data into clinical practice, irregularities in drug response predictions, and the absence of comprehensive and accurate Artificial Intelligence driven solutions to tackle these gaps. In today’s technological world, AI has come forward and has significantly impacted numerous fields of scientific research, and so has impacted the arena of pharmacogenomics too, AI has helped consolidation of large volumes of genomic data to recognize and relate genetic differences with drug response. AI has also facilitated customizing the treatment’s course of action for individuals according to their unique genetic profiles, lowering the possibility of side effects and enhancing the mechanism of action of treatment. Techniques offer the potential to process large-scale genomic datasets, identify novel pharmacogenomic biomarkers, and enable more precise drug response predictions, thus improving personalized medicine approaches. This paper presents a comprehensive literature review of pharmacogenomics, focusing on the translational value, and the challenges in transforming research findings to clinical practice, its ethical, legal, and social issues like data privacy, patents, and the future directions. Among the discussed topics are the basic concepts of pharmacogenomics, the experience of implementing pharmacogenomics in medical practice and in the development of new drugs. We also underline the role of AI in addressing existing constraints by enabling the incorporation of pharmacogenomics into clinical decision-making.