Creating plausible geometric acoustic simulations in complex scenes requires the inclusion of diffraction modelling. Current real-time diffraction implementations use the Uniform Theory of Diffraction (UTD) which assumes all edges are infinitely long. We utilise recent advances in machine learning to create an efficient infinite impulse response model trained on data generated using the physically accurate Biot-Tolstoy-Medwin model. We propose an approach to data generation that allows our model to be applied to higher-order diffraction. We show that our model is able to approximate the Biot-Tolstoy-Medwin model with a mean absolute level difference of 1.0 dB for 1st-order diffraction while maintaining a higher computational efficiency than the current state of the art using UTD.