This research paper provides a comprehensive review of data analytics in healthcare management, focusing on leveraging big data for decision-making. The literature review explores the historical evolution of data analytics, emphasizing its growing importance in clinical support, resource allocation, and operational efficiency within the healthcare sector. The paper discusses fundamental concepts, methodologies, and emerging trends, including the integration of artificial intelligence, real-time analytics, and the impact of wearable technologies. Challenges such as data quality, privacy, and interoperability are identified, along with recommendations for future research. The findings underscore the pivotal role of data analytics in transforming healthcare decision-making processes, with implications for precision medicine, preventive healthcare, and enhanced patient outcomes. The paper also delves into the fundamentals of data analytics, the role of big data technologies, and specific applications within the healthcare decision-making landscape. It highlights the importance of data analytics in clinical decision support, resource allocation, operational efficiency, population health management, patient outcomes, and healthcare delivery and quality improvement. The paper concludes by discussing future trends and directions, including the integration of AI and machine learning, predictive analytics, real-time data analytics, wearable technology, blockchain technology, the integration of social determinants of health, and interoperability and standardization.This research paper provides a comprehensive review of data analytics in healthcare management, focusing on leveraging big data for decision-making. The literature review explores the historical evolution of data analytics, emphasizing its growing importance in clinical support, resource allocation, and operational efficiency within the healthcare sector. The paper discusses fundamental concepts, methodologies, and emerging trends, including the integration of artificial intelligence, real-time analytics, and the impact of wearable technologies. Challenges such as data quality, privacy, and interoperability are identified, along with recommendations for future research. The findings underscore the pivotal role of data analytics in transforming healthcare decision-making processes, with implications for precision medicine, preventive healthcare, and enhanced patient outcomes. The paper also delves into the fundamentals of data analytics, the role of big data technologies, and specific applications within the healthcare decision-making landscape. It highlights the importance of data analytics in clinical decision support, resource allocation, operational efficiency, population health management, patient outcomes, and healthcare delivery and quality improvement. The paper concludes by discussing future trends and directions, including the integration of AI and machine learning, predictive analytics, real-time data analytics, wearable technology, blockchain technology, the integration of social determinants of health, and interoperability and standardization.