Soil moisture is a critical variable in many fields of study, including meteorology, hydrology, and agricultural sciences. In this latter, surface soil moisture (SSM) is crucial for plant growth and development and consequently for yield estimation. Synthetic Aperture Radar (SAR) can be a reliable and trustworthy data source for SSM inversion through the use of empirical, semi-empirical and physically based models. Each of these methods has its own strengths and limitations, depending on the specific application and the environmental conditions. Likewise, there has been a recent surge in attention towards the use of machine learning regression algorithms in the SSM inversion process from SAR data. This work aims to assess the effectiveness of the two algorithms neural network (i.e. single-layer artificial neural network (ANN) and deep neural network (DNN)) for retrieving SSM by utilizing data gathered from diverse rainfall and irrigated (sprinkler) wheat fields located in Tunisia and Morocco. The comparison between predicted and measured SSM showed that the best retrieval results were obtained using sentinel-1 data at VV polarization with R of 0.75 and 0.76 for ANN and DNN, respectively. The RMSE was about \( 0.05 \, {\text {m}}^{3}/{\text {m}}^{3}\) for both algorithms. Overall, the performance of single-layer ANN mimics the highly complex multi-layers DNN in terms of statistical results at VV polarization.