The healthcare business creates a vast amount of data that is critical to improving patient outcomes and scientific understanding. Traditional on-premises data storage and processing options may struggle to keep up with healthcare data's rising volume, variety, and velocity. The purpose of this study is to look at the benefits and drawbacks of cloud-based technologies for data analysis in the healthcare industry. Inspiring new ideas, improving patient care, and providing more precise medical support are just a few of the goals of the latest AI and cloud computing advances highlighted in this article. This article focuses on the application of Hadoop and Cloud Computing in healthcare for big data analytics, namely for monitoring, forecasting, performance monitoring, and management, as well as in other settings such as the intensive care unit. Many applications, tactics, and future directions for big data analytics are covered. Several cloud platforms, like MMAP, are providing timely, dependable, cost-effective, efficient, and patient-centered solutions to these types of public health challenges. On a national or regional scale, these algorithms may also forecast the health repercussions of specific diseases. AI, Hadoop, and cloud computing infrastructure make it easier to share information. This enables healthcare management to undertake the computations required to find the reasonable, important, and factual trends that influence both disaster preparedness and improvement programs.