This paper investigates the increasing impact of digital transformation on the evolution of healthcare systems brought about by the use of new technologies like Artificial Intelligence (AI), Machine Learning (ML), Big Data, and Data Mining. It also evaluates the impact that such technologies have on the practice of medicine today by allowing innovation in finding patterns in massive and complex datasets which has been quite difficult in the past. The research shows that the application and integration of certain data mining techniques—specifically Decision Trees and Neural Networks—improve the accuracy of diagnostics and treatment selection. In addition to Clustering and Association Rule Mining, these methods are proven to greatly improve the effectiveness and precision of the processes within healthcare systems. Still, the research addresses the fact that these technologies can only be fully developed within certain boundaries—such as secure handling of sensitive and private information, integration of multiple data sources, and added value of the information about the patients. In addition, the study examines the use of IoT devices and the growth of AI-based tools as innovative approaches to offer more integrated and individualized healthcare services. These innovations can enhance not only the standard of care but also the health itself through the development of personalized medicine and active measures. The research emphasizes the importance of a comprehensive approach to data standardization and its constant improvement in healthcare data’s unique and ever-changing nature. All in all, this study provides a framework for how the discrepancies between the scientific activities and the actual use of data mining/digital technologies in healthcare may be addressed in the future thereby making healthcare services better and more accessible.