Efficient and informative air quality modeling in future emission scenarios is vital for effective formulation of emission reduction policies. Traditional chemical transport models (CTMs) struggle with the computational demands required for timely predictions. While advanced response surface models (RSMs) were proposed and offered much faster estimates than CTMs, they fall short in providing comprehensive estimates of future air quality due to their simplistic and inflexible structural frameworks. Additionally, current RSMs often have difficulty simultaneously accounting for varying emission variables and the effects of regional transport, which limits their applicability and undermines prediction accuracy. In this study, an informative future air quality prediction model "TGEOS v1.0" based on the Transformer framework is developed as an efficient GEOS-Chem agent model. TGEOS is able to swiftly and accurately conduct online predictions of probability distributions for PM_2.5 and O₃ concentrations under future emission scenarios and capture potential extreme pollution events. The model incorporates sectoral emissions of up to 26 distinct species as well as the impacts of regional emissions and meteorology on pollutant concentrations, enhancing its versatility and predictive accuracy. The spatial and probability distributions predicted by TGEOS are in good agreement with GEOS-Chem, with the correlation coefficients for PM_2.5 and O₃ exceed 0.97 and 0.96, respectively. Notably, TGEOS achieves remarkable computational efficiency, executing one-year predictions in approximately 2.51 seconds. Compared with other machine learning models, TGEOS based on Transformer framework showcases superior performance, underscoring the potential of the Transformer framework in air quality modeling.