In today's interconnected world, Securing Internet of Things (IoT) environments from intrusions is essential. This paper presents an innovative machine learning framework designed for intrusion detection in IoT networks. Using precisely selected datasets, the framework employs data preparation and feature engineering techniques to improve data quality and significance. It combines several machine learning methods to provide reliable intrusion detection. Experimental evaluations show that it performs better than traditional methods, with excellent accuracy, precision, and recall. This work helps to improve IoT security by proposing an effective strategy for protecting IoT ecosystems.