Predicting heavy rainfall remains a significant challenge for meteorological departments as it greatly impacts economies and human lives. Severe rainfall can result in natural disasters like floods and droughts, impacting millions of people globally every year. Precise rainfall prediction is especially important for nations like India, where agriculture serves as a key economic pillar. Due to the atmosphere’s dynamic nature, statistical methods often fall short in achieving high prediction accuracy. The complex, nonlinear characteristics of rainfall data make artificial neural networks a more effective method. This paper reviews and compares various methods and algorithms employed by researchers for rainfall forecasting, presenting the findings in a tabular format to make these techniques accessible to non-specialists.