Astrostatistics is an interdisciplinary field that has emerged in the past two decades in order to study important astrophysical issues using appropriate statistical analysis. As incomplete information is common in many situations, statistical analysis of missing data is an important problem. Missing values can be caused by a variety of issues, ranging from recording issues to instrument restrictions. When data from several archives or instruments is merged, it’s almost inevitable that certain objects will be missing from all of the contributing sources. Standard missing data analysis techniques may not be equally applicable to astronomical data analysis because the concept of “missing at random” is unlikely to apply to such data. In literature, several techniques have been proposed to handle missing values. A review of papers relating to missing information in astronomical data and techniques for analyzing missing data is presented in this study. In addition, this study provides insight into the key features of missing value imputation techniques, their performance, limitations and the most suitable data types for them.