Facial pathologies are common and represent a significant functional handicap when they affect facial movements. Restoring facial expressions in cases of facial paralysis as an example, is of great importance for improving the quality of life and social interactions of patients. The understanding of facial mimic mechanism is still a challenge and crucial to improve surgical or rehabilitation treatments. The objectives are to present the workflow addressing the predictive digital twins for facial mimics from subject specific finite element modelling. The development of a subject specific finite element model derived from medical imaging has been performed [1,2]. Specific MRI protocols were developed allowing to simulate facial mimics from data of a subject and to compare simulated data and experimental data [3]. Then, exploration of facial motion learning capacity by the coupling between the reinforcement learning and the finite element modeling has been performed. A modeling workflow has been proposed. Furthermore, the evaluation of different learning strategies to establish motion patterns of the face during facial expression motions could be assessed. This solution will explore the patient specific facial motions without a priori data from the patient and then provides a set of facial muscle activation and coordination patterns for a specific rehabilitation-oriented movement [4]. The ultimate goal is to enhance predictive and preventive care in personalized facial treatments from diagnosis to treatment (surgical and/or rehabilitation) and patient monitoring.