The collection of reliable and high-quality data is seen as a prerequisite for effective and efficient rail infrastructure and rolling stock asset management to meet the requirements of asset owners and service providers. In this paper, the importance of recovering missing information in railway asset management is highlighted, and the advanced models and algorithms that have been applied to recovering the missed data are analyzed and discussed. Through making comparisons among these models and algorithms, a procedure is proposed to guide selecting the appropriate models based on different data missing scenarios. Using the newly developed framework with one dataset from each scenario, new models with different structures are trained and finally, the most suitable model is selected and utilized to recover the missing data and the selected model's performance is evaluated using the data with known or clearly identified missing data mechanisms. Challenges via application of advanced algorithms for recovering missing data are discussed.