Technological and industrial transformations driven by data science and artificial intelligence are profoundly impacting the field of materials science， presenting both unprecedented opportunities and significant challenges for the innovation of energetic materials. As an emerging technology， machine learning offers novel research paradigm for the molecular design and synthesis of energetic materials. It is expected to solve the long-standing bottlenecks such as low efficiency， high cost， and lengthy development cycles. Although some successful cases have been reported， the application of machine learning across the full research cycle of energetic molecules—design， screening， synthesis， and performance validation—remains in a relatively immature stage compared with the application in other advanced materials domains. This review outlines the current research status of machine learning-assisted development of energetic materials， summarizes its applications in molecular design， single-property prediction， and multi-property simultaneous prediction. Nonetheless， the use of machine learning in design and synthesis of energetic materials with targeted properties remains fraught with challenges. Future efforts should prioritize the control of data quality and the construction of standardization frameworks， the development of interpretable machine learning models， and the establishment of interdisciplinary integration platforms， further promoting the efficient creation of high-performance energetic materials.