MERRA-2 meteorological data is widely utilized across the Indian region to investigate various climatological phenomena, necessitating a thorough evaluation of its accuracy. This study evaluates the performance of MERRA-2 meteorological fields over the Indian region by combining radiosonde measurements with satellite observations from AIRS and TRMM, along with reanalysis data from NCEP/NCAR. Our analysis concentrated on important meteorological variables, such as temperature, precipitation, water vapor, wind components, and tropopause pressure, examining them in multiple seasons and pressure levels. MERRA-2 demonstrates comparable seasonal and spatial variations in temperature relative to AIRS observations, with strong correlations (r² > 0.85) and root mean square errors (RMSE) ranging from 0.9 K to 2.5 K near the surface, decreasing to approximately 1 K at higher altitudes. However, MERRA-2 exhibits a cold bias closer to the surface and warm biases in the upper troposphere. Water vapor profiles reveal a wet bias, particularly in the lower to mid-troposphere, with RMSE increasing with altitude, from less than 20 % at 1000 hPa to more than 75 % at 300 hPa. Significant discrepancies are found in zonal wind estimates in the lower troposphere, especially over the Tibetan region, where MERRA-2 overestimates wind speeds. Below 700 hPa, Zonal winds show mean biases (MB) from −0.7 to 1.5 m s^-1 and RMSEs between 0 m s^-1 and 2.2 m s^-1. Agreement improves above 700 hPa, with MBs ranging from −0.5 to 0.6 m s^-1, and zonal wind estimates outperform meridional winds (RMSE: 0 m s^-1- 4.4 m s^-1). MERRA-2 reasonably captures the spatial distribution and intensity of precipitation but overestimates rainfall over complex terrain during the summer monsoon by up to 20 mm d^-1 compared to TRMM data. Tropopause pressure comparisons show good agreement with AIRS (MB: −2 to 3 hPa; RMSE: 2 hPa–4 hPa), though larger biases are evident against radiosonde data (MB: 11 hPa–29 hPa). These findings underscore the robustness of MERRA-2 in representing regional meteorological variability over the Indian region, while also highlighting specific biases, particularly in the lower troposphere and over complex terrain, that require careful consideration. As MERRA-2 data are frequently used as input for climate and chemical transport models, identifying and quantifying these biases is essential for improving model accuracy and enhancing the reliability of atmospheric simulations. This study offers critical insights for developing more robust modelling frameworks.