<![CDATA[Healthcare databases store various types of data, including patient records, medical imaging, and real-time monitoring data. Efficient data management is crucial for improving patient outcomes and operational efficiency. Traditional methods face limitations in terms of scalability, data heterogeneity, and real-time processing. The main challenge in healthcare database management is the ability to efficiently process and analyze large volumes of heterogeneous data. Existing systems struggle with scalability, data integration, and real-time analytics, leading to delays in decision-making and potential errors in patient care. Methodology this research uses machine learning algorithms to enhance the performance and capabilities of healthcare database systems. Techniques such as data mining, predictive analytics, and anomaly detection are applied to optimize data storage, retrieval, and analysis processes. A comparative analysis is conducted between traditional database management systems and ML-enhanced systems to evaluate improvements in efficiency, accuracy, and scalability. The main objective is to demonstrate how ML can be leveraged to overcome existing challenges in healthcare database management. This includes improving data processing speeds, enhancing data integration from various sources, and enabling real-time analytics for better clinical decision-making. Results the findings show that the integration of ML technology significantly enhances the performance of healthcare database systems. The ML-enhanced systems demonstrated improved scalability, faster data retrieval, and more accurate predictive analytics compared to traditional systems. These improvements facilitate timely and informed decision-making in clinical settings, ultimately leading to better patient outcomes.]]>
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