<![CDATA[This review investigates the transformation of deep learning in a fog computing environment, strongly emphasizing synergy between these enabled technologies and their real-world consequences across various domains. Fog computing is the decentralized approach to data processing, overcoming certain limitations in traditional cloud systems: it reduces latency up to 50%, minimizes bandwidth usage, and alleviates network congestion. Deep learning, known for pattern extraction from complex datasets, enhances real-time analytics and intelligent decision-making in resource-constrained environments. Together, they enable effective processing and prompt decision-making in applications such as anomaly detection in healthcare-for example, arrhythmias with 50% faster response, traffic flow optimization in smart cities, and predictive maintenance in industrial automation, reducing downtime by 60%. Integrating deep learning with fog computing has numerous advantages, such as reducing dependencies on cloud infrastructure, enhancing data privacy, and increasing real-time processing. Yet, several challenges remain, like the resource-limited computational capacity of fog nodes, security vulnerabilities, and the need for scalable and efficient architecture. Recent lightweight model design, federated learning techniques, and hierarchical frameworks are some promising solutions to such challenges. This review synthesizes the current research findings, identifies sector-specific applications, and addresses critical challenges. It also outlines future directions comprising the development of adaptive architectures, privacy-preserving methodologies, and hybrid approaches in artificial intelligence. Meeting these challenges will unlock the full potential of deep learning and fog computing-driving innovation and efficiency across industries.]]>
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