Pilots must manage their workload correctly to achieve a safe operation. However, few studies have attempted to estimate a pilot’s workload in real-time, and there are no established methods of doing so. To provide more direct support for a pilot’s workload management, we attempted to construct a model that estimates the pilot’s future workload using data on various flight-related parameters that can be acquired in real-time. Participants conducted simulated flights using the flight simulator. Based on the data obtained from these simulations, we used machine learning to construct a model to estimate the workload level 30 s later on a five-point scale. This model correctly estimated 32.0% of the test data, and in 72.3% of the test data, the deviation between the subjective value and the estimated value was within one workload level. We implemented a system that presents the estimated workload level to the pilot in real-time, and from the review of a license holder who conducted simulated flights using the proposed system, we confirmed that the system is effective for workload management.