Monitoring the spatio-temporal variation in soil moisture content (SMC) of the surface soil layer is essential for agriculture and water resource management activities, especially in regions where the socio-economic condition and livelihood depend upon agriculture and allied sectors. In the present study, we have compared different machine learning (ML) and linear regression models to estimate the SMC integrating field observed soil moisture and Sentinel-1 SAR data. Total 56 soil samples were collected from the surface soil layer (~ 5 cm) in correspondence with the passing date of the Sentinel-1 sensor over the study area. The surface SMC was estimated for bare soil areas, which was extracted by applying the threshold values on vegetation and water index maps derived from the Sentinel-2 multispectral data. The univariate linear regression with the co-polarized VV band provided higher accuracy compared to the cross-polarized VH band. However, the multiple linear regression with VV and VH bands indicated similar accuracy as obtained by the VV band alone. The random forest model was observed as the best performing ML model for soil moisture estimation (R² = 0.87 and 0.93 during modeling and validation, respectively; RMSE: ~ 0.03). The obtained results indicate well accurate surface soil moisture verified with in-situ information collected during the dry rabi crop season (January to March 2019). The maximum SMC was observed for March, followed by February and January, that corroborated with the total monthly precipitation and irrigation activities. The study highlights the potentiality of ML models and Sentinel-1 SAR data for soil moisture estimation, which is useful for policy-level implications and decision making in agriculture and water resource management activities.