Machine learning techniques are now extensively being used for prediction of long-term and short-term rainfall time series datasets because the lack of data and availability of recorded daily rainfalls may affect the feasibility of a larger range of rainfall-based studies in light of repercussions from climate change and extreme hydro-meteorological phenomena. A daily gridded rainfall time series dataset (2007–2020) over Mizoram state is constructed using data machine learning methods such as Support Vector Regression and Random Forest. For this purpose, the open sources and observed gridded rainfall datasets CHIRPS, APHRODITE, IMDAA re-analysis, and IMD have been utilized. In this study, the statistical evaluation methods have been employed and tested over selected rainfall grids to enhance the accuracy of the datasets. Quantile-quantile (Q-Q) plots were also used to test the accuracy of each dataset in the case of extreme rainfalls with respect to IMDAA gridded rainfall (i.e., IMD Re-analysis), which has been taken as the observed/reference dataset. In this study, the quantile mapping and linear scaling bias correction methods are utilized to correct the rainfall datasets. The predicted rainfall datasets have been compared produced by SVR and RF, and the RF-based predicted rainfall datasets have performed superior than SVR datasets in terms of bias and extremity.