India is one of the leading agricultural countries in the world, and the nation’s economy depends heavily on agriculture. For good crop yield, prediction of precipitation is necessary to increase agricultural output and ensure a supply of food and water to maintain public health. To reduce the issue of drought and floods occurring in the nation, wise use of rainfall water should be planned for and implemented. Numerous studies have been carried out utilizing data mining and machine learning approaches on environmental datasets from various nations in order to forecast rainfall. This study’s primary goal is to pinpoint the amount of rainfall in several regions of India in the past hundred years and apply machine learning techniques to forecast the amount of rain that will fall in a particular month and year in a given region. The dataset was collected from the government site of the rainfall database for performing machine learning techniques. The random forest model’s ensemble approach, robustness to noise, ability to handle nonlinear relationships, feature importance analysis, scalability, and tuning flexibility make it a particularly effective choice for rainfall prediction in this project. Its versatility and performance make it a valuable asset for providing accurate and reliable rainfall forecasts to support decision-making in various sectors, such as agriculture, water resource management, and disaster preparedness.