Partnering with OneSky Flight LLC, this project aims to develop a flight time predictor using various machine learning methods. OneSky Flight LLC's goal is to provide IT support for various private jet companies. Their flight time predictor is mainly used for customer satisfaction and aircraft turn-over efficiency. This project involves creating a potential improvement on the current flight time predictor they use now. Six months of flight data was provided by OneSky Flight LLC; it included attributes such as origin, destination, aircraft type, departure time, and arrival time. The two primary methods tested were neural networks and decision trees. Each method was tested with varying architectures and data structures to determine accuracy. The resulting analyses of the architectures found the XGBoost decision tree to be the highest performing model. Using the results of the architectures, an ensemble model can be developed that incorporates the use of both neural networks and decision trees to further increase the accuracy of the predictor.