<p>Among the different types of droughts, hydrological drought immensely affects the surface water availability in the reservoirs, ponds, and river channels. In this research work, an attempt has been made to determine hydrological drought intensity across the Maharashtra state and the probable hydrological drought categories at various districts. The hydrological drought indicator, standardized runoff index (SRI) in raster cells, was used to map the hydrological drought events, and the respective area was computed. The stochastic processes, such as the Markov chain, have a significant role in tracking and predicting the various stages of hydrological response to drought. By using the Chapman–Kolmogorov equation, the transition probability matrix was calculated between two consecutive years from 2012 to 2023, and the average transition probability matrix was generated. The average drought condition at the district level was computed for each SRI raster dataset, and the same values were used to create a heatmap. Here, 12&#xa0;years of data were used to showcase the average drought category at the district level, and this was employed to execute for computing the next 12 years condition, i.e., till 2035, using maximum likelihood estimation (MLE). To show the hydrological drought condition (HDC) and the pattern in the various districts of Maharashtra with the help of infographics, choropleth mapping is used. It was revealed that, as the river flow occurs through the districts, the changes in HDC reflect from the origin to the end point of the river. At the downward flow, the possibilities of hydrological wetness conditions were high as compared to the origin. To validate the results predicted using the MLE method, the Brier score was used; this has shown values closer to 0, which indicates relative accuracy. The research has the potential to address the severity of hydrological drought and early preparedness for the hydrological drought and management from water security aspects.</p>