Herein, a method for predicting real-time removal rate and friction coefficient between the pad and substrate during chemical mechanical polishing was investigated using only the load currents of two motors of a polisher. Polishers for semiconductor devices are equipped with various sensors, enabling a real-time prediction of the removal amount. The polishers used to polish substrates are not usually equipped with sensors, and the polishing time is fine-tuned by skilled-technicians to achieve the desired substrate thickness. However, since every polisher has some motors, predicting the removal rate and friction coefficient using only the real-time data produced by these motors would be beneficial. This study attempts to predict the removal rate and friction coefficient in long-time polishing using a training dataset obtained from short-time polishing. Results showed that by performing extremely low-pressure, long-time polishing to understand the polisher characteristics and then subtracting the polisher characteristics from the motor information during long-time polishing, highly accurate predictions of the removal rate and friction coefficient within ~94% in percent match (prediction accuracy) between the experimental and predicted values can be obtained. Furthermore, slurry degradation during CMP can be monitored using this prediction method.