This thesis develops a new way to predict the road roughness with Kalman filter. It suggests applying the Kalman filter to predict road condition in suspension system. According to the literature review and to the knowledge of authors, no similar applications of Kalman filter in predicting the road roughness are found at the time of the writing thesis. Most of the prediction nowadays is around the road prediction with GPS. It concentrates on avoiding the road bumps by the operator. This research is brand new in this field. What the authors focus on is to predict the road condition and to pass this information to the control system. By this way, the passenger comfort is improved. This research is practical in transportation industry. Nowadays the passenger comfort is crucial. This road condition predictor can help the vehicle to improve the passenger comfort. Furthermore, this predictor can be adjusted to different road conditions. A suspension system is important to improve passenger comfort. Magneto-Rheological(MR) damper, which is a controllable damper, can improve the performance of the suspension system. This thesis presents a menthod to predict the road condition for MR damper. Firstly, three suspension systems, passive, active and semi-active suspension systems, are evaluated by their costs and performances. The semi-active suspension system has good performance with low cost. This suspension system shows better performance with proper control strategy. Additionally, two different levels of road roughness are simulated by Harmonic superposition method in time domain. One of the road roughness scenarios is chosen to test the prediction method. The road roughness is predicted by a Kalman filter. The result shows that the Kalman filter can estimate the road condition with a high accuracy. The prediction frequency is high in this method. The control strategy can adjust its coefficient based on the high prediction frequency. Thus, the performance of the suspension system is enhanced and the passenger comfort is also improved.