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In today's interconnected world, traditional security measures are increasingly inadequate for safeguarding the rapidly expanding cloud-based Internet of Things (IoT) networks against sophisticated cyber threats. This paper presents “Smart Shields,” an innovative security framework that leverages advanced machine learning (ML) techniques to provide adaptive and real-time defense for IoT ecosystems. Our approach incorporates various ML algorithms, such as Decision Trees, Support Vector Machines, Random Forests, and Principal Component Analysis, to accurately identify and respond to anomalies in IoT networks. To address the limitations of conventional centralized security solutions—high latency, power consumption, and poor scalability—our framework employs a distributed model using fog computing, enhancing threat detection efficiency at the network edge. By integrating advanced ML models and an autonomous, continuously learning security management system, Smart Shields dynamically adapts to new threats, ensuring robust protection of IoT environments. This research demonstrates that Smart Shields offers a scalable, low-latency, and energy-efficient solution to enhance the security and integrity of IoT networks in the face of evolving cyber threats. Furthermore, Smart Shields incorporates an autonomous, continuously learning security management system that dynamically adapts to new and emerging threats. By integrating advanced ML models with distributed computing, our framework ensures robust, scalable, and energy-efficient protection for IoT networks.