Globally, there is a lot of concern about climate change. This study sets out to explain how historical rainfall variations occurred. Pre-monsoon and early monsoon regions will become drier in the future, whereas late monsoon and post-monsoon regions will experience significant variations in rainfall amounts. For meteorologists, predicting rain is a particularly challenging task. In recent years, a variety of models have been applied to assess and accurately forecast rainfall. Climate records can be quite helpful in this regard. Retaining data for a long time can help us predict rainfall more precisely. In this paper, the application of statistical techniques, in particular the linear regression and SVM method is provided for modeling and predicting rainfall across Bangladesh. The rainfall data for the 31 years was provided by the Bangladesh Meteorological Department (BMD), Dhaka. Between 1989 and 2019, this surface-based rain gauge in Bangladesh gathered rainfall data from 8 metrological stations. How much rain falls each month and year has been determined. For each station, the average, median, correlation coefficients, and standard deviation were calculated to evaluate the correctness of the data. It was found that the model's rainfall forecast provided reliable results when the amount of rainfall projected by the model was compared to the amount of rainfall measured by rain gauges at various locations.