Healthcare analytics has changed due to integrating Big Data with Machine Learning (ML) and Fog Computing, allowing real-time decisions and predictive analysis to produce better patient results. Modern healthcare requires innovative solutions to alleviate the current cloud architecture limitations, particularly because of the exponential increment of health data. Such data from EHRs, medical imaging systems, and IoT devices create difficulties because of the systems’ complexity in managing volume, velocity, variety, veracity, and value. Healthcare analytics in traditional systems faces multiple barriers because it requires paying high costs and dealing with data silos, as well as security problems and latency issues. It lacks consistent standards for processing information. Fog Computing implements decentralization through source-based data processing, thus reducing reliance on centralized cloud platforms and supporting real-time medical decisions. Machine learning fulfills its revolutionary function by using supervised and unsupervised learning and deep learning methods to facilitate analytical clinical care, disease prediction, and automated diagnostic procedures. The chapter discusses the primary principles of Big Data healthcare and Fog Computing’s system infrastructure and analytics value while showing how Machine Learning operates in medical diagnosis systems. The analysis includes discussions about actual case studies and anticipated research routes, technical approaches to developing fog-based ML systems, privacy measures, and ethical aspects.