The rapid advancement of the Internet of Things (IoT) has transformed health monitoring by enabling continuous, real-time data collection, especially for tracking sleep quality and overall health. This research aims to address the limitations of conventional models by developing a robust framework for predicting health and sleep quality using stacked ensemble regressors. The study fills a gap in knowledge by focusing on integrating multiple models to enhance prediction accuracy in a highly dynamic and complex dataset generated by IoT devices. To achieve this, an ensemble approach was employed, combining Gradient Boosting Regressor with other predictive models in a stacked architecture. Feature engineering was applied to develop interaction terms, optimizing the model with hyperparameter tuning. Novel elements of the work include the use of feature interactions, subsampling, and shrinkage, which collectively improve model robustness and generalization. The algorithms contributing to the model's novelty include Gradient Boosting for capturing complex dependencies and stacking methods for refining predictions. Results demonstrate that the proposed model outperforms traditional machine learning approaches in predicting sleep quality and health metrics, as evidenced by significant improvements in R2, mean squared error (MSE), and mean absolute error (MAE). These findings underscore the effectiveness of ensemble methods for handling multidimensional IoT data and provide a pathway for real-time, accurate health insights. The implications are significant for preventive healthcare, enabling more personalized and reliable health recommendations based on real-time data.