Turn change prediction and next speaker prediction are two important tasks in multimodal, multiparty human-agent interaction. Predicting a change of dialogue turn and the most probable next speaker can help an agent to decide whether he should contribute to the discussion or wait for someone else to speak. In this research, we propose a machine learning-based approach for both turn change and next speaker prediction. Individual as well as combined models are explored to tackle these tasks. Results show that the proposed models outperform baselines. An ablation study is also performed to measure the importance of different features.