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Smart cities aim to use technology and data to efficiently utilize city resources, improving residents’ quality of life and advancing sustainability. Efficient management of these resources, with the Internet of Things (IoT) playing a crucial role, is essential for enhancing smart cities’ functionality and sustainability. As the number of users grows, providing efficient and timely services becomes increasingly challenging and intelligent management of resources is key to delivering effective IoT services. This paper introduces a novel approach, the Ensemble Fog Layered Service Management (EFLSM) model, designed to manage and optimize resource allocation and usage within a smart city’s fog computing layer. This model addresses challenges such as unnecessary application migrations, and efficient context sharing and fog nodes migration issues. The proposed EFLSM model operates in three stages: Firstly, it assigns service requests to the appropriate fog nodes based upon the available computing resources and context history through a multi-channel queuing model; secondly, it ensures efficient context sharing and migration to address context instance shortages; and thirdly, it guides service request migration to select fog nodes using ensemble machine learning predictions. Experimental results from a hardware test-bed illustrate the deployment of the EFLSM model and demonstrate its superior performance over existing methods like SMCQ, greedy strategies, and random allocation.