Fast radio bursts are a new astronomical phenomenon that have scientists baffled. To detect and analyse these FRBs, upcoming radio telescopes will capture tremendous amount of data. At the scales of these new telescopes, traditional methods of manually analysing the data fail. There is a need for extensive research in the use of machine learning for this task. This dissertation compares the performance of various machine learning algorithms, so they can be scaled to meet the demands of these new telescopes. To that extent, this research explores ways to simulate FRBs for analysis. The study compares four algorithms, namely, K-Nearest Neighbours, Random Forests, Deep Learning, and Convolutional Neural Networks.