Edge cloud or fog/edge computing, also known as fog/edge networking is a decentralized computing infrastructure that extends cloud computing capabilities closer to the data source, typically at the network’s edge. This architectural shift is particularly beneficial for IoT-enabled healthcare (IoTH) networks, which handle voluminous, sensitive, and often latency-critical data. This chapter explores advanced optimization strategies for the computing and communication infrastructure (CCI) in IoTH systems, with methodologies applicable to other critical IoT domains. The primary focus is on minimizing both the initial infrastructure deployment costs and the ongoing operational costs, particularly transmission power, while rigorously ensuring data integrity, low latency, and real-time delivery. To achieve this, a comprehensive binary integer programming formulation is introduced. This model meticulously optimizes the placement of heterogeneous nodes (Primary Secondary Edge Nodes), manages transmission power, and accounts for various operational expenses. Recognizing that planning CCI for resource-efficient IoTH networks is inherently complex and computationally demanding due to the NP-hard nature of the problem, this work leverages sophisticated swarm intelligence-based algorithms. Specifically, the Discrete Fireworks Algorithm (DFWA) and the Discrete Artificial Bee Colony (DABC) algorithm, both enhanced with an ensemble of local search methods, are employed for optimization. Extensive simulation results and statistical analyses demonstrate significant reductions in both power consumption (up to 33%) and overall infrastructure costs, contributing to more cost-effective, resilient, and efficient IoTH healthcare systems. These advancements ultimately enhance data communication reliability, system scalability, and overall operational efficiency, paving the way for more robust IoTH deployments.