Soil moisture plays an essential role in the complex eco-hydrologic processes, such as infiltration, rainfall-evapotranspiration-runoff circulation, photosynthesis, and groundwater recharge. However, the accurate estimation of soil moisture (SM) at regional or larger scale is difficult because SM varies highly over space and time due to heterogeneous land cover and soil properties, and ground measurements are often time-consuming and expensive. Currently, Bangladesh Meteorological Department (BMD) measures SM only at twelve stations which is quite inadequate for assessing large-scale spatial and temporal variation of SM. Thus, satellite-derived soil moisture data products or Global Land Data Assimilation System simulated (GLDAS-2.2) soil moisture dataset with the Gravity Recovery and Climate Experiment Data Assimilation (GRACE-DA) can be promising alternatives to the in-situ measurement for this data-scarce region. In this study, the spatial and temporal variations of SM from GLDAS and Soil Moisture Active Passive (SMAP) satellite were compared against the in-situ measurements from seven agrometeorological stations of Bangladesh. The GLDAS and SMAP products overpredicted the in-situ SM for most of the stations and could capture the temporal dynamics of observed SM with correlation coefficient (R) of 0.36 and 0.17, respectively. Later an Artificial Neural Network model was developed based on soil moisture from both sources (SMAP and GLDAS) and terrestrial water storage from GLDAS to obtain more accurate estimation of SM for this data-scarce region. The ANN model shows an improvement in estimation and predicted SM with R = 0.63 (considering all stations). The results were more promising when separate model is developed for each study site. Incorporating additional climate data (such as precipitation with different lag times) as input improved the accuracy marginally. This study suggests that the release of daily GRACE gravity field solutions in near-real-time may provide a reasonable and continuous estimate of soil moisture in this data-scarce region.