This review explores the transformative impact of Information Technology (IT) on precision medicine and genomics, highlighting the critical roles of bioinformatics, data mining, machine learning, and blockchain in advancing personalized healthcare. These IT-enabled approaches facilitate the analysis, interpretation, and application of vast genomic datasets, enhancing disease prediction, diagnosis, and treatment on an individual level. However, challenges such as data complexity, interoperability, ethical considerations, and the digital divide must be addressed through multidisciplinary collaboration and innovation. Precision medicine, which tailors healthcare to individual characteristics, has evolved significantly, with roots in pharmacogenomics and the completion of the Human Genome Project in 2003. Key concepts in genomics include genetic variation, gene expression, genome sequencing, and bioinformatics. The integration of IT in healthcare has enabled the development of electronic health records (EHRs), health information exchange (HIE), telehealth, big data analytics, and artificial intelligence (AI) and machine learning, all of which are pivotal in precision medicine. Bioinformatics plays a crucial role in genomic analysis, while data mining and machine learning help uncover patterns in genomic and clinical data. Genetic sequencing technologies, such as Next-Generation Sequencing (NGS), have reduced the cost and time of genome sequencing, and data analysis tools are essential for identifying genetic markers. Clinical decision support systems (CDSS) integrate genetic information with clinical data to recommend personalized treatments. The integration of genomic data with EHRs supports personalized care and longitudinal tracking of patient outcomes. Predictive modeling and risk assessment tools use algorithms to estimate an individual's risk of developing diseases, while telemedicine and remote monitoring expand access to precision medicine. Data security and privacy are critical, with encryption and blockchain technologies being used to protect genetic information. Despite these advancements, challenges such as data complexity, interoperability, ethical issues, and the digital divide remain significant barriers. Future directions in precision medicine include emerging technologies like single-cell sequencing, CRISPR gene editing, and AI-driven predictive models. Blockchain technology offers secure data sharing and consent management. Policy and regulatory frameworks must evolve to address ethical, legal, and social implications. Patient engagement and personalized health strategies are essential for the successful implementation of precision medicine. In conclusion, IT-enabled approaches in precision medicine and genomics are reshaping healthcare, offering the promise of personalized, predictive, and preventive care. Addressing challenges through multidisciplinary efforts and ethical considerations is crucial for realizing the full potential of precision medicine.This review explores the transformative impact of Information Technology (IT) on precision medicine and genomics, highlighting the critical roles of bioinformatics, data mining, machine learning, and blockchain in advancing personalized healthcare. These IT-enabled approaches facilitate the analysis, interpretation, and application of vast genomic datasets, enhancing disease prediction, diagnosis, and treatment on an individual level. However, challenges such as data complexity, interoperability, ethical considerations, and the digital divide must be addressed through multidisciplinary collaboration and innovation. Precision medicine, which tailors healthcare to individual characteristics, has evolved significantly, with roots in pharmacogenomics and the completion of the Human Genome Project in 2003. Key concepts in genomics include genetic variation, gene expression, genome sequencing, and bioinformatics. The integration of IT in healthcare has enabled the development of electronic health records (EHRs), health information exchange (HIE), telehealth, big data analytics, and artificial intelligence (AI) and machine learning, all of which are pivotal in precision medicine. Bioinformatics plays a crucial role in genomic analysis, while data mining and machine learning help uncover patterns in genomic and clinical data. Genetic sequencing technologies, such as Next-Generation Sequencing (NGS), have reduced the cost and time of genome sequencing, and data analysis tools are essential for identifying genetic markers. Clinical decision support systems (CDSS) integrate genetic information with clinical data to recommend personalized treatments. The integration of genomic data with EHRs supports personalized care and longitudinal tracking of patient outcomes. Predictive modeling and risk assessment tools use algorithms to estimate an individual's risk of developing diseases, while telemedicine and remote monitoring expand access to precision medicine. Data security and privacy are critical, with encryption and blockchain technologies being used to protect genetic information. Despite these advancements, challenges such as data complexity, interoperability, ethical issues, and the digital divide remain significant barriers. Future directions in precision medicine include emerging technologies like single-cell sequencing, CRISPR gene editing, and AI-driven predictive models. Blockchain technology offers secure data sharing and consent management. Policy and regulatory frameworks must evolve to address ethical, legal, and social implications. Patient engagement and personalized health strategies are essential for the successful implementation of precision medicine. In conclusion, IT-enabled approaches in precision medicine and genomics are reshaping healthcare, offering the promise of personalized, predictive, and preventive care. Addressing challenges through multidisciplinary efforts and ethical considerations is crucial for realizing the full potential of precision medicine.