Droughts are slow-onset disasters and affect developing economies significantly more than developed ones. Changing climatic conditions impact developing economies such as India more than developed ones, and many regions are experiencing water stress and long-term drought. The hydrological drought (represented by lower streamflow than historical) prediction is challenging for data-scare regions such as the Indian subcontinent. Prediction of hydrological drought requires the forecasted streamflow, and the accuracy of the prediction depends on the choice of model, quality and quantity of historical streamflow data, and model parameter calibration. Getting long-term streamflow data for most Indian catchments is the most challenging one. On the other hand, computing meteorological drought is relatively easy as it is the function of simple climatic variables such as daily precipitation and temperature. Studies indicate that the relationship between hydrological drought and meteorological drought exists. However, the degree of prediction of hydrological drought with meteorological drought is difficult as each catchment has different catchment characteristics. In this study, we try to understand how catchments’ attributes, such as soil type, topography, long-term land use, and meteorological drought, help to predict hydrological drought without needing any complex hydrological model setup for each catchment. We first develop a catchment attributes dataset with more than 40 attributes. We then selected thirty years with climatic and streamflow historical data and computed hydrological and meteorological droughts using that. We use the Standardized Streamflow Index (SSI) to characterize Hydrological drought (HD),the Standardized Precipitation Index (SPI), and the Standardized Precipitation Evapotranspiration Index (SPEI) to represent meteorological drought (MD). Use Mann KendalTau statistics, Cohen's Kappa Test, and Granger Causality to find the relation between hydrological and meteorological droughts. To examine HD's lagged propagation to MD. Dynamic Time Warping (DTW) provides optimal alignment between HD and MD. Later, we employ cluster analyses to identify the physio-climatically similar catchment, allowing us to extrapolate the condition of HD in ungauged catchments with similar physio-climatic attributes.