Dust storms and the associated air pollution pose significant environmental and health challenges in Inner Mongolia; however, accurately predicting PM10 concentrations remains difficult because of complex spatio-temporal patterns. This study provides a comprehensive comparison of machine learning and deep learning approaches for PM10 prediction in Inner Mongolia's dust-dominated environment during the spring dust season (March-May, 2021-2024). All models achieved high accuracy (R & sup2; > 0.90) but exhibited distinct strengths: LSTM excelled in temporal pattern recognition, CNN-LSTM in spatial feature extraction, XGBoost in computational efficiency with competitive accuracy, and ERT in capturing regional variations. We considered 15 candidate predictors; a genetic algorithm was then used to select an optimal subset (9 predictors) for the final model training. Bayesian optimization was used for hyperparameter tuning. The LSTM model achieved the highest overall accuracy (coefficient of determination = 0.93, root mean square error = 27.54 & micro;g/m & sup3;), followed closely by the CNN-LSTM model. For practical application, LSTM is recommended as the primary model for hourly PM10 prediction due to its best overall accuracy and stability. XGBoost provides a computationally efficient alternative with competitive skill under low-to-moderate concentrations, whereas ERT is more conservative for exceedance-risk screening but may overestimate high-risk areas. Spatial analysis revealed high pollution probabilities in southern and western Inner Mongolia, where more than 30% of the region has Level-1 PM10 pollution (50-150 & micro;g/m & sup3;) with probability greater than 0.6. Temporal analysis identified March as the peak month for dust pollution, with PM10 concentrations occasionally exceeding 1200 & micro;g/m & sup3; during severe dust events. Although all models tended to underestimate extreme concentrations, tree-based models showed relatively higher sensitivity to high-PM10 episodes.This study establishes a practical framework for dust-related PM10 prediction in arid regions and provides actionable insights for air-quality management and pollution-control strategies.