The modern aircraft has evolved to become an important part of our society. Its design is multidisciplinary in nature and is characterized by complex analyses of mutually interdependent disciplines and large search spaces. Machine learning has, historically, played a significant role in aircraft design, primarily by approximating expensive physics-based numerical simulations. In this work, we summarize the current role of machine learning in this application domain, and highlight the opportunity of incorporating recent advances in the field to further its impact. Specifically, regression models (or surrogate models) that represent a major portion of the current efforts are generally built from scratch assuming a zero prior knowledge state, only relying on data from the ongoing target problem of interest. However, due to the incremental nature of design processes, there likely exists relevant knowledge from various related sources that can potentially be leveraged. As such, we present three relatively advanced machine learning technologies that facilitate automatic knowledge transfer in order to improve design performance. Subsequently, we demonstrate the efficacy of one of these technologies, i.e. transfer learning, on two use cases of aircraft engine design yielding noteworthy results. Our aim is to unveil this new application as a well-suited arena for the salient features of knowledge transfer in machine learning to come to the fore, thereby encouraging future research efforts.