Growing trends towards increased complexity and prolonged useful lives of engineering systems present challenges for system designers in accounting for the impacts of post-design activities on system performance. It is very difficult to develop accredited lifecycle system performance models because these activities only occur after the system is built and operated. Thus, system design and post-design decision making have traditionally been addressed separately, leading to suboptimal performance over the system’s lifecycle. With significant advances in computational modeling, simulation, sensing and condition monitoring, and machine learning and artificial intelligence, the capability of predictive modeling has grown prominently over the past decade. Predictive modeling can bridge system design and post-design stages and provide an optimal pathway for system designers to effectively account for future system operations at the design stage. In order to achieve optimal performance, post-design decisions and system operating performance can be incorporated into the initial design with the aid of state-of-the-art predictive modeling approaches. Therefore, optimized design and operation decisions can be explored jointly in an enlarged system design space. This article conducted a literature review for the integrated design and operation of engineering systems with predictive modeling, where not only the predictive modeling approaches but the strategies of integrating predictive models into the system design processes are categorized. Furthermore, a summary of the challenges and future research directions is provided that encourages research collaborations among the various communities interested in the optimal system lifecycle design.