In this study, an application of support vector machines are presented for fouling detection and estimating the amount of deposit layer development and tube blockage percent at the radiation section of the crude oil preheat furnace. Crude oil preheat furnaces are the main elements in processing crude oil in distillation towers, which may always suffer from fouling and its consequent risks. In order to predict fouling inside the tubes, first by considering independent input parameters effecting the furnace performance and by using a dynamic model of a particular furnace, the behaviors of the furnace in unusual conditions were simulated. The effects of fouling type and its location inside the tubes were considered on the thermal performances and pressure drops of the furnace. In the second part, based on the different fouling scenarios, a fouling detection mechanism was designed. The operational conditions such as pressure drop inside the tubes, temperatures of the tubes and temperatures of the crude oil were employed for fouling detection and evaluating the thickness of deposits. The obtained results indicated the accuracy and feasibility of proposed approach.