Aim: Heavy rains can create a number of tragedies in the rain. Forecasting is essential. The prediction allows people to take preventative steps, and it must produce the desired outcome. Materials and methods: In the proposed work categorizing is performed by adopting a pattern size of n =10 with the g-power value of 80% and datasets are collected from various web sources with recent study findings and threshold 0.05%, confidence interval 95% mean and standard deviation was iterated 20 times to obtain data in the Random Forest algorithm. The Random Forest algorithm and implementation will be used in it. For the implementation, an additional test could be used. Results: It shows a low accuracy of random forest (87.01%) in contrast with the logistic regression by the set of rules (95.25%). There is a statistically insignificant difference between the observed agencies with sizable 0.075 (p>0.05) Conclusion: Prediction is a category of rainfall prediction that indicates that the selection tree seems to generate higher accuracy than the random forest.