Day-of-surgery cancellation (DOSC) in elective surgery occurs in roughly 18% of elective surgeries worldwide. This impacts patient physical health, psychological wellbeing, and social function. Further impacts include reduced health service efficiency and wider economic productivity. There are a range of contributing variables including patient factors, resource constraints, and health service pressures which could be integrated into predictive models. This article describes the protocol for a systematic review to evaluate peer-reviewed original research articles and implementation studies of models to predict DOSC. Such statistical models could, if properly integrated into clinical practice, yield benefits to patients and healthcare providers. The systematic review will provide a comprehensive synthesis of evidence in this area to inform future efforts at gold-standard statistical modelling. Predictor-finding studies, subsequent publications of the same model and publications in which the predictive variables have not been disclosed will be excluded. Searches will be conducted in Medline, Embase, Scopus and Web of science. Risk of bias will be assessed using the prediction model risk of bias assessment tool (PROBAST). Data will be collected on included variables, method of prediction, whether prediction was made at the level of the patient or the system, and training and assessment processes. This data will be subject to qualitative synthesis and used to generate a narrative summary and figures. This systematic review will abide by the 2020 PRISMA guidelines. This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors. This review is registered on PROSPERO, registration CRD42023478984.