This paper provides a detailed examination of machine learning-based approaches designed to enhance the cybersecurity of Internet of Things (IoT) networks. With IoT devices being increasingly targeted by cyber-attacks, this study focuses on leveraging the Ridge Classifier to achieve reliable anomaly detection with an accuracy rate of 97%. This high detection accuracy is complemented by additional security layers, including encryption protocols, firewall integration, and real-time data monitoring, which together form a robust multi-layered defense against both common and advanced threats. The paper also discusses the challenges involved in scaling machine learning solutions like the Ridge Classifier within large IoT networks, where processing vast amounts of real-time data requires substantial computational power. To address these challenges, future work is suggested to focus on optimizing detection efficiency and exploring hybrid models that could further enhance adaptability to emerging threats, ultimately aiming for more scalable and responsive IoT security solutions.