Shape memory elastomers (SMEs) are a class of intelligent materials characterized by their ability to deform and recover shapes under applied force and external stimuli. Heat and ultraviolet radiation are examples of the most common external stimuli. With the emerging prevalence of internet of things devices and the ensuing need for smart materials and structures, SMEs provide significant opportunities to support the development of novel applications in robotics, remotely actuated systems, and packages, including those promised for the space industry. To harness the immense potential in the emerging applications of these materials, one approach is the systematic multi‐scale modeling coupled with artificial intelligence‐assisted design leading to the development of next‐generation intelligent systems. This review covers several aspects of the synthesis/materials chemistry and applications of SMEs with a view towards enabling such an approach. The synthesis procedures emphasizing dynamic covalent bond reactions are reviewed. Then, liquid crystalline elastomers are introduced as a specific elastomeric material class that exhibits excellent shape memory characteristics and distinctive transition temperatures. The utilization of advanced manufacturing methods such as additive manufacturing, three‐dimensional printing, and the emerging four‐dimensional printing technologies assisted by machine learning are detailed in producing and predicting SMEs. Finally, the current trends in the use of SMEs are summarized in areas of industrial and space engineering and biomedical applications.