The fast and frequent movement of vehicles creates many challenges in vehicular networks, such as handling regular topological changes. Predicting a vehicle's future location by preemptively adjusting to changes caused by vehicle movements is a potential solution to many of these problems. However, reliably predicting vehicle movement remains an issue due to its stochastic nature. This paper proposes a prediction method that probabilistically analyzes the vehicle's current movement to determine the vehicle's future steps. This is accomplished by combining the Kalman filter and hidden Markov model to include both temporal and historical data, thus improving the prediction reliability by the consideration of more system variables. The proposed approach is tested and compared to other recent approaches through simulation using SUMO and NS-2. The results show a 50% prediction error reduction of the proposed approach in comparison to other methods.