The aerodynamic model of a combat aircraft is essential for its success and competitiveness compared to other combat aircraft. This thesis aims to research the most optimal machine learning model to create an aerodynamic model of a combat aircraft. The very large but still sparse, highly nonlinear dataset forms a challenge for using specific machine learning models. Tree-based models, artificial neural networks (ANNs), and Bayesian neural networks (BNNs) have been identified to be individually capable of modelling the aerodynamics of combat aircraft. For ANNs, additional research was performed into genetic algorithms, as a robust hyperparameter optimisation method. Transfer learning, which reduced the training time by around 14%. Finally, adding gradient information of the training data as an additional input, reduced the mean squared error (MSE) by 7%. Scalable BNNs, which used Bayes-by-backprop, were developed to handle the large dataset. The uncertainties coming from the BNN were overconfident in the results compared to the tolerances of the dataset. The uncertainties showed only a weak correlation with the MSE of a given prediction. Finally, to leverage each of the model's advantages, a stacked model was created which improves the predictive performance on average by 27% compared to the best base model in terms of the MSE on the test dataset. With the stacked model implemented, each of the aerodynamic coefficients could be predicted with over 97% of the predictions of the test data within the tolerance. This is an average increase of 0.5% for each of the aerodynamic coefficients compared to the best base models. Due to the strict regulations related to this aerodynamic model, the machine learning model that is created cannot replace the aerodynamic model at this time. However, the implementation of this machine learning model allows engineers to design the aerodynamic model faster, and with greater precision.