To prevent undesirable effects during milling processes, online predictions of upcoming events can be used. Process simulations enable the capability to retrieve additional knowledge about the process, since their application allows the generation of data about characteristics, which cannot be measured during the process and can be incorporated as pre-calculated features into the analysis. Furthermore, sensor technologies were used as reasonable data sources for analyzing different monitoring scopes of milling processes. Machine learning-based models utilize data, acquired by various available data sources, to generate predictions of upcoming events in real-time. In this paper, we propose a novel approach for combining simulation data with sensor data to generate online predictions of process forces, which are influenced by tool wear, using an ensemble-based machine learning method. In addition, a methodology was developed in order to synchronize pre-calculated simulation data and streaming sensor measurements in real time. Milling experiments using ball-end milling tools with varying cutting speeds and tooth feeds showed the robustness of the approach in enhancing the prediction accuracy compared to only using one of each data source.