Missing data are a universal data quality problem in many domains, leading to misleading analysis and inaccurate decisions. Much research has been done to investigate the different mechanisms of missing data and the proper techniques in handling various data types. In the last decade, machine learning has been utilized to replace conventional methods to address the problem of missing values more efficiently. By studying and analyzing recently proposed methods using machine learning approaches, vital adoptions in accuracy, performance, and time consumed can be highlighted. This study aimed to help data analysts and researchers address the limitations of machine learning imputation methods by conducting a systematic literature review to provide a comprehensive overview of using such methods to impute missing values. Novel proposed machine learning approaches used for data imputation are analyzed and summarized to assist researchers in selecting a proper machine learning method based on several factors and settings. The review was performed on research studies published between 2016 and 2021 on adopting machine learning to impute missing values, focusing on their strengths and limitations. A total of 684 research articles from various scientific databases were analyzed using search engines, and 94 of them were selected as primary studies. Finally, several recommendations were given to guide future researchers in applying machine learning to impute missing values.INDEX TERMS Systematic literature review, data imputation, data mining, missingness, data preprocessing, data quality.

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The end goal of technological advancement used in crisis response and recovery is to prevent, reduce or mitigate the impact of a crisis, thereby enhancing sustainable recovery. Advanced technological approaches such as social media, machine learning (ML), social network analysis (SNA), and big data are vital to a sustainable crisis management decisions and communication. This study selects 28 articles via a systematic process that focuses on ML, SNA, and related technological tools to understand how these tools are shaping crisis management and decision making. The analysis shows the significance of these tools in advancing sustainable crisis management to support decision making, information management, communication, collaboration and cooperation, location-based services, community resilience, situational awareness, and social position. Moreover, the findings noted that managing diverse outreach information and communication is increasingly essential. In addition, the study indicates why big data and language, cross-platform support, and dataset lacking are emerging concerns for sustainable crisis management. Finally, the study contributes to how advanced technological solutions effectively affect crisis response, communication, decision making, and overall crisis management.