Asphalt concrete is one of the most widely used materials in modern road construction. Predicting its functional properties is crucial in the design of new asphalt concrete mixtures. However, current prediction models are limited in accuracy and applicability due to the complex nature of asphalt concrete properties. This thesis researches the use of machine learning algorithms to greatly improve upon existing prediction models. The input is limited to standardized test results in line with Dutch regulations, the output focusses on functional design parameters including stiffness, fatigue resistance, water sensitivity and resistance to permanent deformations. The performance of several machine learning algorithms and the effects of different regression methods are compared. Furthermore, a solution is found for the inverse problem, which allows for greater flexibility when using the models to design new asphalt concrete mixtures. The results show that machine learning algorithms outperform traditional models on accuracy while simplifying the model input parameters. Machine learning algorithms were also able to predict a greater range of output parameters, most of which with a high accuracy. The analysed possibility of modelling asphalt concrete mixtures directly from their desired functional properties is shown to be promising. The proposed machine learning models and their inverse problem counterparts have the potential to greatly improve the accuracy and practical usability of the prediction of asphalt concrete properties, ultimately leading to better mixture design and more durable roadways.