The objectives of this research are to develop an understanding of how the elements of data science are applied in the managerial aspects of healthcare organizations and how these can be enhanced through the use of high end methods from the field of computer science. Closely tied to algorithms like Decision Trees, Support Vector Machines, the adopted Random Forest and Neural Networks, the work’s objectives involve increasing operations productivity and improving patients’ conditions. In this research, these algorithms are applied to vast healthcare datasets that showed enhanced grounds in the aspects of accuracy and resource utilization. For example, the Random Forest Model readily reached an accuracy of 92. 5% in determining readmissions of patients; it was more efficient by 15% contrasted to conventional techniques. Neural Networks achieved sensitivity of 88. Including organism 7% in the technological field such as, early disease detection, showing their ability to deliver personalized care for patients. Comparative analysis with other methodologies prevailing in the existing literature further substantiated the efficacy of these data-driven approaches in healthcare facilities. The research also covers efficacy and issues of ethics where much consideration is given to data privacy and security when deploying AI technologies. In conclusion, this research gives a comprehensive framework of the application of data analytics in the management of healthcare organizations as it opens a channel for the further improvement of the healthcare services.