Big data analytics in healthcare holds significant promise for improving patient outcomes, reducing costs, and enhancing decision-making. This review explores the potential of big data analytics in healthcare, discussing its benefits, challenges, and applications. The healthcare industry generates vast amounts of data, including electronic health records, clinical data, and patient information, which can be analyzed to identify patterns, trends, and insights. Big data analytics can support clinical decision-making, disease surveillance, population health management, and other healthcare functions. The paper outlines the architectural framework and methodology for big data analytics in healthcare, emphasizing the need for scalable, secure, and user-friendly platforms. It discusses the challenges of managing and analyzing big data, including data quality, privacy, and the need for standardized governance. Examples of big data analytics applications in healthcare include improving patient outcomes, reducing hospital readmissions, and identifying potential health risks. Big data analytics has the potential to transform healthcare by enabling more accurate diagnoses, personalized treatment plans, and efficient resource management. However, challenges such as data integration, data quality, and the need for advanced analytics tools must be addressed to fully realize the potential of big data in healthcare. The paper concludes that while big data analytics is still in its early stages, it has the potential to significantly impact healthcare delivery and outcomes.Big data analytics in healthcare holds significant promise for improving patient outcomes, reducing costs, and enhancing decision-making. This review explores the potential of big data analytics in healthcare, discussing its benefits, challenges, and applications. The healthcare industry generates vast amounts of data, including electronic health records, clinical data, and patient information, which can be analyzed to identify patterns, trends, and insights. Big data analytics can support clinical decision-making, disease surveillance, population health management, and other healthcare functions. The paper outlines the architectural framework and methodology for big data analytics in healthcare, emphasizing the need for scalable, secure, and user-friendly platforms. It discusses the challenges of managing and analyzing big data, including data quality, privacy, and the need for standardized governance. Examples of big data analytics applications in healthcare include improving patient outcomes, reducing hospital readmissions, and identifying potential health risks. Big data analytics has the potential to transform healthcare by enabling more accurate diagnoses, personalized treatment plans, and efficient resource management. However, challenges such as data integration, data quality, and the need for advanced analytics tools must be addressed to fully realize the potential of big data in healthcare. The paper concludes that while big data analytics is still in its early stages, it has the potential to significantly impact healthcare delivery and outcomes.