To effectively detect and clean anomalous flight trajectory data, issues such as duplication, missing values, and errors that frequently occur during data collection were studied. A data anomaly detection and cleaning method with high accuracy and robustness was proposed based on machine learning. First, duplicate removal, interpolation, and normalization of the data were performed by combining linear interpolation and the min-max scaling algorithm. Next, a flight trajectory prediction model was constructed based on GRU. Finally, the SVDD model was utilized for anomaly detection of flight trajectory data. When the actual data significantly deviated from the predicted data, the predicted data was used for replacement to achieve the cleaning effect. The study results showed that compared to other LSTM algorithm models, the proposed method yielded flight trajectory data with higher accuracy after cleaning, with an average F₁ score of 0.932, closely approximating the original flight trajectory data. By examining three attack methods of position shift (tampering), altitude deviation (interference), and route crossing (replacement), the robustness of the proposed method was demonstrated. The method can accurately predict flight trajectories, effectively detect and clean anomalous data, improve data quality, and contribute to accurate analysis of flight operations.