This work demonstrates that the machine learning application can be used in the prediction of the Air Quality Index (AQI) at Dome A, East Antarctica. The daily meteorological parameters for March 2025, including rainfall, temperatures, wind direction, wind speed, humidity, pressure and solar irradiance data were obtained from the NASA POWER database. K-means clustering was applied in segmentation according to similar meteorological patterns. Using an empirical AQI estimation equation based on weather variables, initial AQI values were simulated. A Random Forest regression model was trained on this estimated AQI and the meteorological input. The model was evaluated with five-fold cross-validation, yielding good results with a root mean square error (RMSE) of about 2.4 AQI units averaged across folds, and an RMSE of about 1.3 and the coefficient of determination (R²) was high near 0.81 on separated test data. As shown in Fig. 3, that the predicted AQI values follows the simulated AQI values within the "Good" air quality category. The precipitation and solar irradiance came out as key factors (refer Fig. 5). These results underline that even in remote regions such as Antarctica, machine learning can effectively simulate AQI using meteorological data alone.