Emerging as a key paradigm in contemporary smart healthcare systems, fog computing provides distributed data processing capability that help to reduce the inherent latency and bandwidth constraints of cloud computing. Emphasizing their importance to changing healthcare environments, this chapter investigates the fundamental ideas, architectures, and models of fog computing. Examined closely are many fog computing models—client–server, peer-to-peer, publish-subscribed architectures—each suited to certain operational settings and latency needs. Virtual machines (VMs), Docker, Kubernetes, and serverless computing—among other virtualization and containerization technologies—help fog networks allocate resources more effectively and scalable. The conversation also covers hardware deployment issues, defining how edge servers, intelligent gateways, and micro-data centers may maximize computing processes. Software-defined networking (SDN) among other communication models is examined for their part in dynamic routing, adaptive bandwidth allocation, and congestion avoidance. Using real-world applications of fog computing in healthcare, a focused case study shows its efficiency in predictive analytics and real-time patient monitoring. In fog computing, security is top priority and calls for sophisticated cryptographic methods, blockchain-based authentication, artificial intelligence-driven anomaly detection, Trusted Execution Environments (TEE) to protect private medical information. While these security systems improve data integrity and privacy, reaching total confidence in distributed fog architectures remains difficult, especially in mission-critical applications subject to strict regulatory criteria. By tackling these technological aspects, this chapter offers a whole picture of how fog computing shapes solutions for next-generation healthcare. It underlines the requirement of ongoing innovation in networking, security, and computational methodologies to guarantee the resilience, dependability, and compliance of fog-based healthcare systems.