Abstract: Traditional airflow field measurement methods for cotton picker fans, such as Pitot tubes, only capture discrete point data and fail to obtain full-flow field information. Computational Fluid Dynamics (CFD) simulations, while enabling full-flow field analysis, are limited by high computational costs and long calculation cycles, making real-time monitoring unfeasible. To address these issues, this study aimed to develop a fast and accurate dynamic monitoring method for the airflow field at the outlet of cotton picker fans based on Digital Twin (DT) technology. Firstly, a high-fidelity CFD model of the centrifugal fan was established, and unsteady simulations were performed under rotational speeds ranging from 1000 to 4000 r/min to acquire full-flow field data, including velocity and pressure distributions. Secondly, four surrogate models—Polynomial Response Surface (PRS), Radial Basis Function (RBF), Kriging (KRG), and Support Vector Regression (SVR)—were compared to select the optimal model for fast airflow prediction. Fuzzy C-Means (FCM) clustering was introduced to reduce computational costs by screening representative core sampling nodes from the original dataset. Thirdly, the optimized KRG model was encapsulated into a .NET standard class library using Matlab, and hybrid programming with C# was adopted to develop a Windows Forms (WinForm) Human-Machine Interface (HMI) platform. Finally, a pneumatic conveying test bench was constructed, equipped with a PNP Hall effect sensor for rotational speed measurement and a planar guide rail system for high-precision wind speed data collection at key nodes, to validate the system's performance. CFD simulations indicated that the airflow velocity distribution at the fan outlet became increasingly non-uniform with rising rotational speed, with a maximum velocity difference of 50 m/s at 3500 r/min. Among the four surrogate models, the KRG model demonstrated the best performance, featuring lower Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) compared to the others. FCM clustering reduced the number of training samples by 8.84%, improved response speed by 8.86%, and decreased prediction error by 3.9353% compared with uniform sampling. The final monitoring system, constructed with 821 clustered representative nodes, achieved an average response time of less than 1 second across all tested rotational speeds. Experimental validation showed that the average prediction error between the system outputs and measured values was within 9.26%, with the lowest error of 4.90% at 4000 r/min. Dense grid tests at 3500 r/min confirmed that the relative error of most measuring points was below 8%, even in regions near the volute tongue where airflow separation and eddies occurred. The proposed DT-based dynamic monitoring method for cotton picker fan airflow fields effectively integrates CFD simulation, surrogate modeling, and FCM clustering. It realizes rapid and accurate full-flow field monitoring of cotton picker fans, with real-time responsiveness and prediction accuracy meeting the operational requirements of agricultural machinery. This method provides technical support for the monitoring and optimization of pneumatic conveying systems in cotton pickers and offers a transferable reference for airflow field monitoring in other agricultural equipment, promoting the application of digital twin technology in agricultural engineering.