Protecting Internet of Things (IoT) environments from intrusions is essential in today's digitally linked society. A novel machine learning framework for intrusion detection in Internet of Things systems is presented in this study. By utilizing carefully curated datasets, we apply data preprocessing and feature engineering techniques to enhance data quality and relevance. Our framework incorporates various machine learning algorithms to achieve precise intrusion detection. Experimental results highlight its superior performance over baseline methods, demonstrating high accuracy, precision, and recall. This paper presents a novel automated learning model for IoT security detection.