Abstract: To effectively utilize dust suppression measures in fully mechanized excavation faces, this study proposed a rapid prediction algorithm for the flow field based on proper orthogonal decomposition (POD) and machine learning. First, computational fluid dynamics (CFD) technology was used to simulate the air flow field and dust concentration field under various conditions, generating high-dimensional flow field data. Then, the POD method was applied to reduce the dimensionality of this data, extracting core modes that captured the main characteristics of the flow field and producing basis function modes and mode coefficients. Machine learning techniques were subsequently used to predict the mode coefficients that accounted for over 90% of the total energy under different conditions, enabling predictions of mode coefficients for unknown conditions. Finally, by reconstructing the flow or dust concentration field data using the predicted mode coefficients and basis function modes, rapid and accurate predictions for the flow field in excavation faces were achieved. The results showed that the numerical simulation model for the excavation face had a relative error within 3%, accurately reflecting the actual air flow and dust distribution. Selecting the first five modes for the flow field and the first seven modes for the dust concentration field balanced the accuracy and efficiency of POD reconstruction. The support vector machine (SVM) model outperformed the Random Forest and Neural Network models in predicting mode coefficients. For 60 different conditions, the relative errors between the POD and SVM-predicted flow velocity and dust concentration, and the CFD results, were 0.36 m/s and 86.24 mg/m³, respectively. The average prediction time for the flow and dust concentration fields was 73 seconds, achieving high-precision, rapid predictions for airflow and dust concentration in mine excavation faces.