Predicting when and how much rain will fall is a difficult and unpredictable process that has far-reaching consequences for human civilization. Predictions that are both timely and accurate may be used to proactively reduce casualties and property damage. This research provides a series of experiments that employ popular machine learning methods to construct models that predict whether or not it will rain the next day in major Australian cities based on meteorological data for that day. Modelling inputs, modelling approaches, and pre-processing procedures are the focal points of this comparative analysis. The findings compare and contrast the performance of different machine learning methods in making accurate weather predictions using a variety of assessment measures.