Rainfall is a form of precipitation and is responsible for providing most of the freshwater for animals and plants. Machine learning can be used to analyze data trends to develop a model. Deep learning on the other hand focuses more on using images specifically to analyze data. Trying to understand the patterns of rainfall to predict it has proven to be a difficult undertaking, as seen by the various research using machine learning and deep learning for this problem. When implementing a solution to this rainfall prediction problem, a vast amount of computational resources are usually required to execute it. Thus arises a need to properly store and analyze the data to effectively approach the prediction aspect. This paper investigated the comparison of predictive models for rainfall prediction using big data technologies and radar rainfall images. The literature on state-of-the-art prediction models was investigated and compared to survey which models could achieve satisfactory prediction results in combination with big data technologies. The models chosen were Random Forest Regressor and Deep LSTM and were used to predict 1,2, and 3 days ahead using monthly rainfall data. Results from this study showed that the Deep LSTM model performed better than the Random Forest Model for sequence lengths of 4, 8, and 12 when predicting 1, 2, and 3 months ahead.