The prediction of rainfall is a daunting task due to its non-linear nature and the impacts of climate change. This is crucial for agriculture, flood mitigation and general safety of citizens. Different classification algorithms including Decision Trees, Support Vector Machines are being computed based on predictive performance and confidence. Also, some of the methods used in the analysis of the dynamics of interregional rainfall include k-means clustering and Principal Component Analysis. The use of these machine learning techniques into easy-to-use web applications which have been modelled using Flask helps farmers and the public in making informed decisions. This review work highlights reasons of developing effective rainfall estimation procedures that will help farmers in implementing sustainable practices and enhance the level of readiness of communities to extreme weather conditions. In the end, this review attempts to evaluate the best rainfall estimation techniques, current challenges and present future research possibilities in this important area.