In the production printing industry, printing speed of not only plain paper but also special paper has improved. After toner fixing process, when heat is applied to toner to fix it on paper, the toner on the paper stick to each other on outlet tray leading to toner blocking problem in high-speed printing. To control a paper cooling device, accurate prediction of the outlet paper temperature is useful. This, however, is not so easy; printing conditions and paper types are too diverse to conduct the experiments and the mechanism of the printer is also too complex to develop the physical model. The machine learning (ML) algorithm to predict the paper temperature was proposed under the limited printing conditions. In this research, the ML model that could improve prediction accuracy and generalization capability was developed by selecting appropriate paper properties for the input.