Groundwater is a significant resource that supports almost one-fifth population globally, but has been is diminishing at an alarming rate in recent years. To delve into this objective more thoroughly, we calculated interannual (2002–2020) GWS (per grid) distribution using GRACE & GRACE-FO (CSR-M, JPL-M and SH) Level 3 RL06 datasets in seven Indian river basins and found comparatively higher negative trends (-20.10 ± 1.81 to -8.60 ± 1.52 mm/yr) in Basin 1–4 than in Basin 5–7 (-7.11 ± 0.64 to -0.76 ± 0.47 mm/yr). After comparing the Groundwater Storage (GWS) results with the CHIRPS (Climate Hazards Group Infrared Precipitation with Stations) derived SPI (Standardized Precipitation Index) drought index, we found that GWS exhausts analogously in the same period (2005–2020) when SPI values show improvement (~ 1.89–2), indicating towards wet condition. Subsequently, the GWSA time series is decomposed using the STL (Seasonal Trend Decomposition) (LOESS Regression) approach to monitor long-term groundwater fluctuation. The long term GWS rate (mm/yr) derived from three GRACE & GRACE-FO solutions vary from -20.3 ± 5.52 to -13.19 ± 3.28 and the GWS mass rate (km3 /yr) lie in range of -15.17 ± 4.18 to -1.67 ± 0.49 for basins 1–3. Simultaneously, in basin 4–7 the GWS rate observed is -8.56 ± 8.03 to -0.58 ± 7.04 mm/yr, and the GWS mass rate differs by -1.71 ± 0.64 to -0.26 ± 3.19 km3 /yr. The deseasonalized GWS estimation (2002–2020) states that Himalayan River basins 1,2,3 exhibit high GWS mass loss (-260 to -35.12 km3 ), with Basin 2 being the highest (-260 km3 ). Whereas the Peninsular River basin 4,6,7 gives moderate mass loss value from -26.72 to -23.58 km3 . And in River basin 5, the GWS mass loss observed is the lowest, with a value of -8 km3 . Accordingly, GPS (Global Positioning System) and SAR (Synthetic Aperture Radar) data are considered to examine the land deformation as an effect due to GWS mass loss. The GPS data acquired from two IGS stations, IISC Bengaluru and LCK3 Lucknow, negatively correlates with GWS change, and the values are ~ -0.90 to ~-0.21 and ~-0.7 to -0.4, respectively. Consequently, correlation between GWS mass rate (km3 /yr) and the SAR (Sentinel-1A, SBAS) data procured from Chandigarh, Delhi, Mehsana, Lucknow, Kolkata and Bengaluru shows ~ 72 – 48% positively correlated area (PCA). The vertical velocity ranges within ~ -94 to -25 mm/yr estimated from PCA. There is an increase in population (estimated 2008–2014) in Basin 1 & 2. Likewise, the correlation coefficient ( ) between GWS change and the irrigational area is positive in all seven basins indicating significant depletion in GWS due to an uncalled hike in population or irrigational land use. Similarly, the positive linear regression (R 2 ) in Basins 1–3 also indicates high depletion in GWS. But basins 4–7 observe negative linear regression even after increasing population, which implies a control on the irrigational land use, unable to determine the GWS change at local scale and heterogeneous aquifer distribution. Therefore, if such unsystematic groundwater storage variation is not controlled on time, then very soon in the future, India might reach a deadlock state of water shortage.