<![CDATA[This paper targets the development of advanced machine learning strategies for fog computing systems and is designed to further enhance current mechanisms related to resource allocation. Fog computing represents the extension of cloud facilities to network edges with increased data processing, allowing minimal latency for applications that need real-time processing. This is a review underlining deep learning as one of the basic tools through which neural networks predict the resource usage and optimization of resource allocation with its dynamic adaptation to modifications within the network conditions. The paper reviews techniques such as Convolutional Neural Networks, Recurrent Neural Networks, and Generative Adversarial Networks that are explored for their roles in enhancing efficiency, privacy, and responsiveness within the realm of distributed environments. These findings reveal that deep learning significantly enhances operational performance, reduces latency, and strengthens security in fog networks. By processing data locally and autonomously managing resources, these strategies ensure efficient handling of diverse and dynamic demands. It concludes that the integration of machine learning into fog computing forms a scalable and robust framework toward meeting modern challenges imposed by digital ecosystems, enabling smarter real-time decision-making systems at the edge.]]>
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