As an emerging data-driven technology， machine learning provide a promising pathway for the intelligent research and development of energetic materials. However， data scarcity and heterogeneity have become the core bottleneck that restricts modeling accuracy and practical application. Focusing on the acquisition path and the existing of energetic material data， this review evaluates the mainstream data optimization strategies from two perspectives： quantity expansion and quality improvement. For data quantity expansion， recent advances in SMILES enumeration， generative adversarial networks， and transfer learning are introduced for enhancing model generalization ability. For data quality improvement， the roles of outlier detection， standardized preprocessing， and feature engineering in improving model robustness and interpretability are discussed. It is shown that effective data optimization can not only alleviate data limitations but also significantly enhance prediction stability and structural extrapolation capabilities under small-sample and structurally complex conditions. Finally， future directions are proposed， including the development of high-throughput experimental platforms， unification of data standards， and establishment of intelligent closed-loop systems. It is expected to provide a feasible roadmap and methodological reference for advancing the data ecosystem and intelligent design of energetic materials.