The global impact of the COVID-19 pandemic has done great damage to air transportation. Demand for airline transportation has declined for reasons such as quarantine practices by countries, curfews, the economic recession, and the transfer of meetings to digital platforms. This situation has also led to a change in individuals' preferences for air transport. The most noticeable change in air transport is the tendency of individuals to use air transport privately to minimize the health risks that face-to-face contact can pose. Individuals who avoid commercial air transport where public transportation is available have shifted to private airline transportation. For these reasons, a forecast study was conducted in this study so that a private airline could provide accurate flight schedules in the future. For the forecast study, the number of aircraft types for 2022 was determined by obtaining data on the number of aircraft by passenger capacity, the number of flights, and the number of passengers for 2019-2021 from the airline company. Support Vector Machines (SVM), Gaussian Process Regression (GPR), Regression Trees, and Ensemble Learning models from machine learning methods were used for the forecasting study. The performance evaluation of the models used was compared. The model results with the highest performance evaluation were used. According to the results obtained, it has been found that there will be an increase of approximately 7% in the number of flights for 2022 compared to the pre- pandemic period. The findings provided important information for the company's future fleet planning.