The current research attempts to present a modeling framework for determining soil moisture conditions by using remotely sensed imagery products. In this way, identifying various pixels with similar patterns from satellite images could be a reliable method to have an appropriate view over the soil moisture condition of a particular region. In this context, an artificial intelligence-based self-organizing map (SOM) method is employed to classify homogenous pixels over Phoenix, which is located in the south of Arizona, utilizing parameters extracted from satellite images. The central pixels of clusters are selected as the cluster indicator, with one from each cluster. Then, feed-forward neural networks (FFNNs) consisting of three layers of input, hidden, and output are trained by employing the extracted satellite images time series of the central pixels of the clusters. Finally, the soil moisture conditions of the representative pixels of the clusters are simulated by the trained models. The results reveal the suitability of SOM-based clustering to identify the specific points by which soil moisture can represent the soil moisture condition over the related regions. The proposed methodology and obtained results can be further used to provide a cost-effective method to determine the soil moisture condition of the region by reducing the costs of monitoring.