In today’s world, the IoT continues to expand and a wide variety and large number of devices are being interconnected. At the same time, development of sophisticated threats makes the security of IoT networks more important as well as challenging. This paper explores how machine learning (ML) and deep learning (DL) can improve IoT security frameworks. We start by understating why should ML be used for this purpose and the various ways in which it is currently being used. We introduce a specialized ML-based anomaly detection system design to enhance the security measures of IoT. Our approach uses various techniques of ML, such as neural networks, multivariate correlation analysis, and reinforcement learning, to devise tailored security solutions that accommodate the diverse needs of IoT environments. We use the IoT-23 dataset for an overall preprocessing and model evaluation process that also helps train and validate our models precisely. We find that decision trees and random forests are very efficient for detecting anomalies and significantly enhance the security levels of the IoT network. This study strongly highlights the importance of ML in developing dynamic, efficient, and scalable security strategies that are capable of protecting complex IoT systems for threats. By integrating advanced ML techniques, we can create a more secure and strong IoT ecosystem, which is essential for the increased reliability and growth of IoT technologies.