This research addresses the imperative challenge of resource allocation efficiency in face detection tasks within the Internet of Things (IoT) paradigm by incorporating Fog Computing. The architecture proposed employs smart offloading of tasks, dynamic profiling of resources, as well as a new strategy that is multi-algorithmic in order to enhance efficiency, scalability, as well as reliability in detection of faces in IoT. Specifically, the architecture employs the Energy Valley Optimizer (EVO) as a form of energy-efficient allocation of resources, the Fire Hawk Optimizer (FHO) as a form of adaptive as well as strategic allocation of tasks, as well as the Artificial Bee Colony (ABC) algorithm as a form of decentralized as well as swarm-based optimization. The hybrid strategy efficiently solves dynamic task offloading, dynamic profiling in real time, as well as adaptive optimization in distributed Fog Computing scenarios. This integration promises unprecedented levels of optimization and adaptability, enabling dynamic allocation of processing, storage, and communication resources based on real-time demands. The comprehensive framework presented herein contributes to advancing IoT-based face detection, paving the way for enhanced real-time applications across diverse domains.