Flight schedule planning is a crucial aspect in the air transportation industry to ensure operational efficiency and customer satisfaction. One of the mathematical methods that can be used in such planning is polynomial interpolation. This study aims to analyze the application of the polynomial interpolation method in optimizing flight schedules, especially to predict departure and arrival times based on historical data. Polynomial interpolation is used because of its ability to model non-linear relationships from a series of data points. In this study, the data used included actual flight times on a specific route over a specific period. The Lagrange and Newton interpolation method was applied to build a predictive model of flight schedules. The results show that polynomial interpolation can provide a fairly accurate prediction of flight time, with minimal deviation compared to the actual schedule. Additionally, this method helps in detecting frequent anomalies and delays, allowing for better schedule planning. However, computational complexity increases as the amount of data grows, which becomes a challenge in large-scale deployments. Thus, polynomial interpolation can be an effective tool in planning flight schedules, especially for airlines in improving punctuality and operational efficiency. This research is expected to contribute to the development of a decision support system in flight schedule management.