As the heart of an aircraft, the operational condition of an aero-engine ensures flight reliability and passenger safety, so it is critical to monitor its conditions. The prediction-based condition monitoring methods have received more attention due to their advantages of adaptability and high accuracy. In particular, multi-step prediction can monitor aircraft operational conditions in advance to make timely decisions. However, the prediction accuracy decreases as the prediction steps increase due to the lack of extraction capacity and the accumulation of errors; it is significant to improve the precision of the multi-step prediction. Thus, this paper proposes an Informer-based multi-step prediction method for aero-engine condition monitoring. Firstly, data preprocessing is performed, including missing value handling, standardization, correlation analysis, and data reconstruction to obtain high-quality data for accurate model building. After that, an Informer model is built and optimized for multi-step prediction for timely monitoring of flight conditions, which improves the primitive self-attention mechanism and increases model accuracy and inference speed. Finally, the real flight data is applied to detect the superiority of the proposed approach. The experimental results show that the Informer method outperforms the comparative approaches for multi-step prediction of aero-engines, which provides a timely and reliable scheme for condition monitoring.