Dynamic product transitions are essential for achieving high product quality and reducing production costs. However, optimizing dynamic product transitions is a challenging task due to the complex dynamics of the process and the uncertainty in the measurements. In this work, a data-driven Bayesian approach for optimal dynamic product transitions is proposed. The approach is based on a dynamic optimization problem that is solved using a Bayesian optimization algorithm. One of the advantages of this approach for process optimization tasks is that it does not require a first-principles dynamic mathematical model. The approach is applied to three case studies. The results show that the proposed approach finds optimal transition trajectories meeting product composition requirements using smooth control actions. The approach is also able to cope with noisy measurements, which is an important feature in real-world applications. The proposed approach has several advantages including being data-driven, able to cope with noisy measurements.