The continuous improvement of machine learning (ML) methods could change the approachesfor the design and development of Vertical Take Off and Landing (VTOL) and drones and thereforelead to possible changes in certification and aviation standards. To analyze this topic practicallyand effectively, the idea is to develop a case study of a DT of a Twingtec drone. It is carried out byenhancing an initial physics-based mathematical model, exploiting data from high-fidelitynumerical simulations, computational fluid dynamics (CFD), and structural dynamics, as well asfrom flight testing. The “enhancement” concerns both accuracy and scope and is carried out viaconventional and ML techniques, each exploited at its best. The final digital technology (DT) isexpected to be a hybrid surrogate model-based DT, in which the necessary accuracy is providedby the initial physics-based model coupled with several surrogate models. The expected result is a living DT able to reliably explore the entire flight envelope as well as thedesign space by accounting for changes in mass, centre of gravity, and configuration. The DT assuch is expected to not only support certification but also continued airworthiness and follow thedrone through its operative life. The main objective of this research is to assess the impact of the deployment of digital solutionsin VTOLs and drones development, processes, and operations on the whole airworthinesscertification approach. It includes the safety standards and regulatory materials and how theycould change. The use of digital solutions for the certification of drones and/or their componentsand related equipment could be a new method of compliance demonstration. The potential ofthis method is increased efficiency and decreased risks in flight testing. The project is set as a case study on the Twingtec prototype. Finally, a roadmap to implement theneeded aviation regulation changes and training materials to share the knowledge are released.