Background: Despite the abundance of published studies on prediction models for diagnosing Traditional Chinese Medicine (TCM), there remains a lack of comprehensive assessment regarding reporting and methodological quality, as well as an absence of examination into the objectivity of linguistic aspects within these studies.

Methods: The PubMed, Cochrane Library, Web of Science, CNKI, VIP, and WANFANG databases were systematically searched from inception to October 30th, 2023, to identify studies developing and/or validating diagnostic and prognostic TCM prediction models using supervised machine learning. PROBAST and TRIPOD were employed to assess the reporting and methodological quality of identified studies. A previous article about spin in prognostic factor studies already identified several practices, which we modified for our data extraction the present study was registered on PROSPERO with the registration number CRD42023450907.

Results: 35 and 19 eligible studies published in Chinese and English were identified respectively from 1746 records. The clinical diseases with the most publications were diabetes (n = 7, 14.8%), coronary heart disease (n = 6, 11.1%), and lung cancer (n = 5, 9.26%). Primary analysis and sensitivity analysis confirmed that the reporting and methodological quality of included studies were correlated (rs = 0.504, p < 0.001). The quality of the CM prediction model requires improvement by including a structured title, participants and predictor’s selection, statistical analysis methods, model performance and interpretation. Two studies (4.55%) recommended the model to be used in daily practice lacked any external validation of the developed models. Six studies (13.63%) made recommendations for clinical use in their main text without any external validation. Reporting guidelines were only cited in one study (1.85%).

Conclusion: The available evidence indicated TCM information can provide predict information for different diseases, but the scientific quality of published studies needs to be improved.