Predictive modelling is crucial for understanding and adapting to diverse climate and rainfall patterns, particularly in the context of climate change. In an effort to overcome these issues, the focus of this survey is on the use of machine learning approaches. For many types of sectors including agriculture, disaster prevention, and water resource management, accurate rainfall forecasts are crucial. The introduction of ML approaches is a result of the fact that traditional meteorological models frequently fall short in capturing complicated weather-related processes. In this study, a survey and analysis of the various architectures and ML models, together with their accuracy and performance outcomes, are discussed.