Computer-aided design based on high-fidelity physics-based models often requires a substantial investment of computation time. This is prohibitively expensive for routine tasks such as design space exploration and optimization, especially for the topology design of electric machines which typically requires thousands of simulations. Surrogate models are a popular approach to reduce this computational burden by providing a fast data-driven approximation of the time-consuming simulation model. This project concentrates on the development of a data-efficient surrogate-based topology optimization framework coupled with an accurate multiphysics model for high-performance additively manufactured electric machines. The key points in this regard are: 1) Multiphysics models of electric machines employing additive manufacturing: The creation and smart parameterization of a high-fidelity model considering thermal, structural, and magnetic effects. 2) Data-efficient topology optimization: A sequential design scheme will be developed based on surrogate models to run only the necessary simulations with optimal accuracy-speed trade-off. The outcome of this project will promote smart design methodologies for complex multiphysics-based models to improve the performance of electric machines and leverage the benefits of additive manufacturing, which have important theoretical research significance and engineering application value.