Diabetes prediction is crucial for improving public health by enabling early detection and intervention. The integration of IoT in healthcare allows continuous monitoring of vital health indicators, such as glucose levels and BMI, through connected devices. This real-time data, combined with machine learning, enables more accurate and personalized interventions. In this study, we propose a Bagging Classifier model enhanced with IoT-driven data, achieving 99% accuracy. Trained on a balanced diabetes dataset from Kaggle, the model outperforms traditional classifiers, demonstrating its potential for diabetes prediction and management in IoT-enabled healthcare systems.