In indoor environments, air quality significantly impacts human health and well- being, with carbon monoxide (CO) posing a particular hazard due to its colorless and odorless nature and potential to cause severe health issues. Integrating the Inter- net of Things and remote sensing technologies has revolutionized data monitoring, collection, and evaluation, especially within the context of ‘smart’ homes. This study leverages these technologies to enhance indoor air quality monitoring. By collecting data on key indoor atmospheric quality indicators—carbon dioxide (CO2), meth- ane (CH4), alcohol, liquefied petroleum gas (LPG), particulate matter (PM1 and PM2.5), humidity, and temperature—the study aims to predict indoor carbon mon- oxide levels. A custom dataset was compiled from August to October, consisting of 61,710 observations recorded at one-minute intervals. The methodology employs a stacking ensemble approach, integrating multiple machine learning models to boost prediction accuracy and reliability. In the stacking ensemble, six distinct models are employed: Random Forest, Multi-Layer Perceptron, Lasso, Elastic Net, XGBoost, and Support Vector Regression. Each model is individually trained and fine-tuned using the Grid Search method to optimize parameter combinations. These optimized models are then combined in the stacking ensemble, which achieves a Mean Squared Error (MSE) of 0.0140, a Root Mean Squared Error (RMSE) of 0.1185, and a Mean Absolute Error (MAE) of 0.0291. The results demonstrate that the proposed system significantly enhances the precision of CO prediction, underscoring its critical role in air quality surveillance within smart environments.
