Formation pressure or pore fluid pressure below the ground surface should be estimated before drilling a wellbore, so that we can select the appropriate drilling mud window and wellbore wall for drilling operation. The drilling strategy of a wellbore will be made suitable and most principled decision. In addition, due to the relatively high cost of direct pore pressure measuring instruments, it is cost-effective to use less expensive methods that provide a continuous range of pore pressure data thought of reservoir interval. In this context two direct and indirect approaches are usually performed. Using seismic and well survey data (indirect method) and also pressure testing (direct method) is used. The main purpose of this paper is to use full set as well as RFT data to determine the wellbore pore pressure. For this purpose, according to the information in the wells drilled in an oil field, we estimate wellbore pore pressure using machine learning algorithm. We will compare the obtained result with a conventional pore pressure prediction methods such as the Bowers method.