This study provides an empirical investigation into the effectiveness of several deep learning models in forecasting ambient concentrations of particulate matter with a diameter of less than 2.5 micrometers (PM_2.5), nitrogen dioxide (NO₂), and sulfur dioxide (SO₂). These pollutants are critical due to their adverse impacts on human health and the environment. We evaluated four distinct models: Graph Convolutional Network (GCN), Empirical Mode Decomposition combined with GCN (EMD+GCN), Ensemble Empirical Mode Decomposition with Gated Recurrent Unit and GCN, and GCN with an attention mechanism (GCN_ATT). Through rigorous computational experiments, the models were assessed against multiple statistical metrics including Mean Absolute Error (MAE), Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE), and the Coefficient of Determination (R²). The EEMD+GRU+GCN model consistently outperformed the others across all pollutants, demonstrating the lowest MAE, indicating its strong predictive accuracy. Similarly, it maintained the smallest MSE, suggesting it was particularly adept at reducing the influence of larger errors in predictions. Moreover, it achieved the lowest MAPE across the datasets, confirming its robustness in percentage terms relative to the scale of the actual values, a critical indicator of practical applicability for air quality forecasting. The GCN model, while foundational, showed significant limitations, especially in the prediction of NO₂ and SO₂, as evidenced by its negative R² values, indicating a poor fit that was outperformed by simple average models. The GCN_ ATT model did not show the expected improvement that the attention mechanism might promise, suggesting that additional fine-tuning or structural model changes are required. In conclusion, the integration of ensemble empirical mode decomposition techniques with advanced neural network architectures such as GRUs and GCNs provides a compelling approach to air quality forecasting. The proposed model’s ability to capture complex spatiotemporal dependencies in environmental data makes it a promising tool for environmental monitoring and policy-making, offering significant benefits for public health and ecological protection.