Health Data Management (HDM) is a crucial component of today’s healthcare involving the effective collection, storage, and analysis of patient data. With the increasing volume of large-scale medical data, conventional cloud-based systems encounter latency, security, and real-time processing challenges. Fog computing has been seen as a viable paradigm, overcoming the limitations of centralized cloud infrastructures and edge devices, with localized data processing, low latency, and high security. This chapter explores how fog computing and machine learning techniques can be combined for healthcare applications to support improved decision-making, predictive analytics, and real-time monitoring. Specifically, supervised learning techniques aid in disease diagnoses and risk predictions unsupervised learning enables anomaly detection and patient segmentation, semi-supervised learning improves medical image analysis with limited labeled data, and reinforcement learning optimizes treatment planning and resource allocation. These approaches enhance diagnostic accuracy, reduce latency in healthcare decision-making, and improve patient care efficiency. Furthermore, challenges in health data management, such as interoperability, scalability, and data privacy, are presented alongside implementation approaches for secure and efficient healthcare analytics.