Demand for predictive powertrain control is rapidly increasing with the recent advancement of Advanced Driving Assistance Systems (ADAS) and Autonomous Driving (AD). The full or semi-autonomous functions could be leveraged to realize better user acceptance as well as powertrain efficiency of the connected vehicle utilizing the proposed Drive Horizon. The sensors of automated driving provide perception of surrounding driving environment which is required to safely navigate the vehicle in real-world driving scenarios. The proposed Drive Horizon provides real-time forecast of driving environment that a vehicle will encounter during its entire travel. This paper summarizes the vehicle’s future speed prediction technique which is an integral part of Drive Horizon for optimized energy control of the vehicle. The prediction model has been developed that integrates information from multiple sources including vehicle GPS, traffic information and map data. Recurrent Neural Networks and Bayesian approaches including generative models have been studied for predicting the vehicle speed. In addition, utilization of connected data (live traffic and map) to enable long prediction horizons has also been considered in this study compared to the conventional using of in-vehicle sensors such as camera or radar. The developed speed prediction technique can be effectively integrated with vehicle’s energy management to improve its energy efficiency. The effectiveness of the proposed speed prediction technique has been verified by testing the prediction accuracy on different routes for the prediction range of 1 kilometer.