Background: More than 278 million cases and more than 5.4 million deaths due to coronavirus disease (COVID-19) were reported worldwide by the end of 2021. More than 34 million cases and more than 478,000 deaths have been reported in India. Epidemiologists, physicians and virologists are working on a number of conceptual, theoretical or mathematical modelling techniques in the battle against COVID-19. Protocol: This systematic review aims to provide a comprehensive review of published mathematical models on COVID-19 in India and the concepts behind the development of mathematical models on COVID-19, including assumptions, modelling techniques, and data inputs. Initially, related keywords and their synonyms will be searched in the Global Literature on Coronavirus Disease database managed by World Health Organisation (WHO). The database includes searches of bibliographic databases (MEDLINE, Scopus, Web of Science, EMBASE etc.,), preprints (MEDRXIV), manual searching, and the addition of other expert-referred scientific articles. This database is updated daily (Monday through Friday). Two independent reviewers will be involved in screening the titles and abstracts at the first stage and full-texts at the second stage, and they will select studies as per the inclusion and exclusion criteria. The studies will be selected for their quality, transparency, and ethical aspects, using the Overview, Design concepts, Details (ODD) protocol and International Society for Pharmacoeconomics and Outcomes Research-Society for Medical Decision Making (ISPOR-SMDM) guidelines. Data will be extracted using standardized data extraction tools and will be synthesized for analysis. Disagreements will be resolved through discussion, or with a third reviewer. Conclusions: This systematic review will be performed to critically examine relevant literature of existing mathematical models of COVID-19 in India. The findings will help to understand the concepts behind the development of mathematical models on COVID-19 conducted in India in terms of their assumptions, modelling techniques, and data inputs.