Abstract: The Air Quality Index (AQI) serves as a crucial indicator of dynamic trends in air pollution. However, achieving high prediction accuracy remains challenging due to the complexity of the atmospheric system, which is driven by intricate interactions between air pollutants and meteorological conditions. To address this and achieve precise air quality forecasting, this paper proposes a multi-strategy Improved Moss Growth Optimization (IMGO) algorithm designed to optimize a Bidirectional Gated Recurrent Unit (BiGRU) network. First, strategies including Fitness-Distance Balance (FDB), adaptive t-distribution, and Opposition-Based Learning (OBL) are integrated to enhance the optimization precision and convergence speed of the algorithm. Evaluations on six standard benchmark functions-supported by statistical results, convergence curves, and box plots-demonstrate that IMGO exhibits superior global optimization capabilities compared to the standard MGO. Second, IMGO is utilized to optimize the hyperparameters of the BiGRU model, significantly bolstering its stability. Finally, precise data fusion is accomplished leveraging the nonlinear mapping capability of the optimized BiGRU. The proposed IMGO-BiGRU model, alongside standard BiGRU and MGO-BiGRU, was applied to AQI prediction tasks. Experimental results indicate that IMGO-BiGRU achieves a high prediction accuracy of 0.982, with a Mean Absolute Error (MAE) of 9.710, a Mean Absolute Percentage Error (MAPE) of 8.107%, and a Root Mean Square Error (RMSE) of 17.660. These findings confirm that the proposed model delivers high precision and robust error reduction in air quality forecasting. Read More