It has been reported that about 50% of the total amount of fuel in an oil refinery plant is consumed in crude oil and vacuum distillations. Thus, the heat exchanger networks of crude oil distillation units significantly affect the overall energy efficiency and CO2 emissions of refinery plants. Crude oil fouling in heat exchanger networks is one of the most troublesome problems in crude oil refineries. It reduces heat transfer amount or blocks the flows in tubes, leading to requiring additional fuel for the furnace following heat exchanger networks. Therefore, many cleaning methods have been developed. Mechanical cleaning of heat exchangers is the most effective method to mitigate the fouling in heat exchangers. However, it is necessary to open heat exchangers for cleaning. So, the timing of mechanical cleaning is limited because the normal refinery operation must be stopped. Therefore, to keep the good energy and economic performance of the refinery, it is necessary to predict the appropriate maintenance timing and to conduct a suitable cleaning. To find a suitable cleaning schedule or timing, the authors proposed a method for predicting fouling resistance in near future for crude oil fouling from actual online plant data in this research. The prediction results are in good agreement with the measured results, which demonstrate that the proposed method is effective for predicting the cleaning timing for the industry in the future.