Avian influenza is an infection of birds caused by the Influenza A virus, which due to crowded conditions and occupational exposure in live poultry markets, has jumped the species barrier to humans. With an estimated case fatality rate of up to 60%, it is vital to map the existing digital technologies that may be utilized to improve disease monitoring and health outcomes for avian influenza. This scoping review aimed to identify which digital technologies may improve disease prevention, detection and control, and could be used as a basis for strengthening health systems. A search was conducted on PubMed and Web of Science for studies that reported the utilization of digital technologies with specific reference to avian influenza. Search dates ranged from 2009 (January) to 2017 (July). Data was extracted into a summative table, citations managed using EndNote software and data synthesized through grouping digital technology domains, using narrative and graphical methods. The scoping review identified 111 relevant studies, and revealed data modelling (n=72) and novel technologies (n=15) referring primarily to diagnostic tools, to be the most utilized technologies in tackling avian influenza. A large portion of the data-modelling domain was compromised of computer-assisted mathematical modelling (n=42) including mathematical modelling (n=8), simulation modelling (n=14) and spatioal-temporal modelling (n=20), primarily used to estimate outbreak distribution according to migratory patterns and transmission dynamics. A major challenge reported was poor biosecurity measures of poultry markets. Digital technologies indicated potential in improving disease detection, control and prevention, particularly through the use of data modelling with meteorological data sets. However, it became evident, that to maximize potential of these digital technologies better implementation of biosecurity measures would be necessary in majorly affected regions such as Asia.