The utilization of statistical analysis is crucial in acquiring the necessary data prior to formulating any hypotheses. It ensures that the results will be of superior quality. The utilization of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks and Climate Data Record (PERSIANN-CDR) dataset yields satisfactory outcomes in comparison to the conventional methods of human data collection or station-based measurements. This study seeks to assess the monthly, seasonal, and annual variations in the Central-India (CI) and West-Coast (WC) regions between the years 1983 and 2020, utilizing PERSIANN-CDR precipitation data. The utilization of the Mann–Kendall and Modified Mann–Kendall methodologies is subsequently employed to identify patterns in the data, while the Standardized Precipitation Index is computed for monthly, seasonal, and annual timeframes. The outcomes are subsequently examined and presented via Innovative Trend Analysis and the hydroTSM software. The present study examines data collected from a total of 1705 sites, employing both the Mann–Kendall and Modified Mann–Kendall statistical tests. The majority of them demonstrate a lack of alteration in the pattern. The annual precipitation in the region has experienced a 0.5% annual rise. The yearly precipitation in the CI-WC arena is recorded to be approximately 1303 mm, which accounts for a mere 0.038% of the overall annual rainfall. In contrast, the Standardized Precipitation Index, Innovative Trend Analysis (ITA), and hydroTSM indicate a little decrease in precipitation. In general, the results are commendable and provide evidence of the influence of global warming on the area. The maps illustrate a notable gap in average rainfall quantities between the lowest and highest values observed from 2012 to 2020, accompanied by an expanded area experiencing drought conditions.