The rapid rise of the Internet of Things (IoT) has revolutionized connectivity across various domains, including smart homes, healthcare, and industrial systems. However, the large-scale integration of heterogeneous devices has significantly increased security vulnerabilities and cyberattack risks. Traditional intrusion detection systems (IDS) are often insufficient for IoT environments due to limited device resources and dynamic network behavior. This study proposes a machine learning–based IDS for detecting and classifying malicious activities in IoT networks in real time. Supervised learning algorithms, including Decision Tree, Random Forest, and Support Vector Machine (SVM), were employed to analyze network traffic and identify anomalies. Experimental evaluation using benchmark IoT datasets showed that the Random Forest model achieved the best performance with an accuracy of 98.1%, detection rate of 98.2%, precision of 98.0%, recall of 98.1%, and a low false positive rate of 1.9%. Comparative analysis demonstrated that the proposed approach outperformed conventional IDS techniques in both detection capability and reliability. These results highlight the effectiveness of intelligent learning models in enhancing IoT network security and supporting trustworthy network operations.