Crystal density is an important parameter for predicting the detonation performance of energetic materials (EMs). Many studies have shown that the theoretical calculation methods are able to figure out accurate densities of CHNO contained EMs. In this work, we overview and categorize some reliable crystal density calculation methods, including isosurface of electron density method, group addition method, molecular surface electrostatic potentials method, crystal packing method and quantitative structure-property relationship method. Among these methods, the effectiveness of molecular volume-based methods depends on its capability to estimate inter- and intramolecular interactions. It is challenging to accurately describe the hydrogen bonding and van der Waals interactions. Due to the huge structure group spaces and highly complex potential energy surface, the crystal packing methods based on empirical forcefields are computationally expensive and lacking accuracy usually. The group addition approach cannot distinguish conformers and polymorphs, and may be unreliable for novel or special energetic materials, which are absent from accurate empirical parameters. The disadvantage of quantitative structure-property relationship method is that it is difficult to give the physical meaning of the equation. The bottleneck of insufficient experimental data and poor model accuracy needs to be solved. Nevertheless, numerous artificial intelligence methods, such as artificial neural networks, genetic algorithm, multiple linear regression, machine learning, have made great achievements in the relationship between properties and structure, facilitating the development of energetic materials based on the materials genome concept and serving as a main tendency in future.