NASA's Cyclone Global Navigation Satellite System (CYGNSS) mission has gained significant attention within the land remote sensing community for estimating soil moisture (SM) by using the Global Navigation System Reflectometry (GNSS-R) technique. CYGNSS constellation generates Delay-Doppler Maps (DDM)s, containing important earth surface information from GNSS reflection measurements. Many previous studies considered only designed features from CYGNSS DDM such as the peak value of DDM, whereas the whole DDM image is affected by SM, topography, inundation, and overlying vegetation. In this paper, a deep learning (DL)-based framework is presented for estimating SM products in the Continental United States (CONUS) by leveraging spaceborne GNSS-R DDM observations provided by the CYGNSS constellation along with other remotely sensed geophysical data products. A data-driven approach utilizing convolutional neural networks (CNNs) is developed to determine complex relationships between the reflected measurements and surface parameters which can help to provide improved SM estimation. The CNN model is trained jointly with three types of processed DDM images of Analog Power, Effective scattering area, and Bistatic Radar Cross-section (BRCS) with other auxiliary geophysical information such as elevation, soil properties, normalized difference vegetation index (NDVI) and vegetation water content (VWC). The model is trained and evaluated using from the Soil Moisture Active Passive (SMAP) mission's enhanced SM products at a 9 km × 9 km resolution with VWC less than 5 kg/m<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula>. The mean unbiased root-mean-square difference (ubRMSD) between concurrent CYGNSS and SMAP SM retrievals from 2017 to 2020 is 0.0366 <inline-formula><tex-math notation="LaTeX">$m^{3}/m^{3}$</tex-math></inline-formula> with a correlation coefficient of 0.93 over 5-fold cross-validation and 0.0333 <inline-formula><tex-math notation="LaTeX">$m^{3}/m^{3}$</tex-math></inline-formula> with a correlation coefficient of 0.94 over year-based cross-validation at spatial resolution of 9 km × 9 km and temporal resolution same as the CYGNSS mission.