The rapid proliferation of the Internet of Things (IoT) has revolutionized connectivity across various sectors, but it has also introduced significant security vulnerabilities. As IoT networks expand, they become prime targets for increasingly sophisticated cyberattacks, underscoring the critical need for effective intrusion detection systems (IDS) capable of protecting these environments. In response, this paper introduces a novel hybrid machine learning framework, the Concatenated Ensemble Model, which combines the strengths of Decision Trees, Gradient Boosting, and AdaBoost to improve the detection and classification of IoT-based attacks. The model is rigorously evaluated on multiple IoT datasets, with a focus on identifying anomalous activities using performance metrics such as accuracy, precision, recall, F1 score, and Cohen’s Kappa. Our results demonstrate significant improvements in detection rates, showcasing the model’s robustness and adaptability in diverse IoT environments. This research not only advances the state-of-the-art in IoT security but also provides valuable insights into the practical implementation of ensemble learning techniques for intrusion detection. By enhancing both the precision and resilience of attack detection, the proposed approach contributes to a more secure IoT landscape, addressing the growing challenges of cyber threats in this domain.