Prediction of ego-vehicle speed for powertrain control has drawn attention to a means to improve the energy efficiency of vehicles. In particular, the understanding of driving situations using Intelligent Transport System (ITS) information can help improve the prediction of accuracy. In this study, a velocity prediction algorithm based on Markov chain is proposed. However, when various ITS information is added, the size of the prediction algorithm rapidly increases, which increases a computational time and is difficult to implement to real-time systems. To solve the problem of the increased computational power, this paper proposes a velocity prediction algorithm based on a fuzzy Markov chain. This algorithm, designed for a vehicle driving on a specified route, combines a fuzzy Markov chain and a speed constraint model. The fuzzy Markov chain stochastically predicts an ego-vehicle's speed within a constraint area and solves the problem of increasing algorithm size when adding various pieces of input data. The speed constraint model, which is estimated by empirical matrices, estimates the constraint area used in the fuzzy Markov chain. Through simulation, the algorithm is evaluated to reduce computational time by 85.5% whilst maintaining a prediction accuracy of 99.1%.