Abstract The global climate is undergoing significant changes. Over time, there have been substantial in the weather. Climate change has caused rainfall to become unpredictable. The frequency of severe weather phenomena such as drought and flood has escalated due to climate change in Pakistan. Demanding more accurate and timely rainfall prediction. Rain forecasting is essential for strategic purposes such as agriculture, water resources management and agricultural design. The inherent non-stationary component in the rainfall time series hinders the effectiveness of models for hydrologists and assessors of drought risk. We propose a FBP model approach for predicting rainfall to tackle the problem of forecasting. Our research model involves forecasting the likelihood of rain the following analyzing data from the past dataset from 1960 to 2020. The FBP model is mostly used for time series data, and the result was a 99% confidence level. The standard deviation (94.34) and the RMSE and MAE values are around 45 and 50.6, respectively. Such a model result is quite good, and a recommendation for the Metrological department to use this for forecasting rainfall. he model’s performance will significantly enhance the accuracy of the rain forecast.