Abstract-Before the evolution of the Wireless Sensor Networks (WSN) technology, many production wells in the oil and gas industry were suffering from the gas hydration formation process, as most of them are remotely located away from the host location. By taking the advantage of the WSN technology, it is possible now to monitor and predict the critical conditions at which hydration will form by using any computerized model. In fact, most of the developed models are based on two well-known hand calculation methods which are the Specific gravity and K-Factor methods. In this research, the proposed work is divided into two phases; first, the development of a three prediction models using the Neural Network algorithm (ANN) based on the specific gravity charts, the K-Factor method and the production rates of the flowing gas mixture in the process pipelines. While in the second phase, two WSN prototype models are designed and implemented using National Instruments WSN hardware devices. Power analysis is carried out on the designed prototypes and regression models are developed to give a relation between the sensing nodes (SN) consumed current, Node-to-Gateway distance and the operating link quality. The prototypes controller is interfaced with a GSM module and connected to a web server to be monitored via mobile and internet networks.