Abstract: This thesis introduces the implementation of different supervised learning techniques for producing accurate estimates of soil moisture content using empirical information, including meteorological and remotely sensed data. The models thus developed can be extended to be used by the personal remote sensing systems developed in the Center for Self-Organizing Intelligent Systems (CSOIS). The different models employed extend over a wide range of machine-learning techniques starting from basic linear regression models through models based on Bayesian framework, decision tree learning, and recursive partitioning, to the modern nonlinear statistical data modeling tools like artificial neural networks. Also, ensembling methods such as bagging and boosting are implemented on all models for considerable improvements in accuracy. The main research objective is to understand, compare, and analyze the mathematical backgrounds underlying and results obtained from different models and the respective improvisation techniques employed.