Significant climate change in recent times has led to large spatial and temporal rainfall variability over the regions. The monthly rainfall data at the rain gauge stations of SPSR Nellore district of Andhra Pradesh were collected for the period of 1990–2022 and used in this study. The period of rainfall data has been divided into two sets—training period (1990–2012) and validation or testing period (2013–2022). The annual rainfall data were used in the study from the collected monthly rainfall data and their statistical parameters such as mean, standard deviation, coefficient of variation, coefficient of skewness and coefficient of Kurtosis are calculated. By using annual rainfall data, good fits of the observed data are obtained using probability distributions, which was done using MATLAB. Probabilistic distributions of rain gauge stations have been done using the Smirnov–Kolmogorov test by making comparisons with Weibul’s plotting position. The data mostly followed log-normal distributions for training and validation periods baring a few exceptions. This study aims to cluster the rain gauge stations for SPSR Nellore district based on statistical parameters and probability distribution functions. In each case, the district is divided into three clusters. These methods showed inconsistent results in the clustering of rain gauge stations. The rain gauge stations have finally been grouped based on their position in the maximum number of methods in which these stations lie in a particular cluster.