Objectiv To provide a comprehensive overview of model performance and predictive efficacy of machine learning techniques to predict septic shock in children, in order to target and improve the quality and predictive power of models for future studies. MethodsTo systematically review all studies in four databases (PubMed, Embase, Web of Science, ScienceDirect) on machine learning prediction of septic shock in children before April 1, 2024. Two investigators independently conducted literature screening, literature data extraction and bias assessment, and conducted a systematic review of basic information, research data, study design and prediction models. Model discrimination, which area under the curve (AUC), was pooled using a random-effects model and meta-analysis was performed. Subgroup analyses were performed according to sample sizes, machine learning models, types of predictors, number of predictors, etc. And publication bias and sensitivity analyses were performed for the included literature. Results A total of 11 studies were included, of which 2 were at low risk of bias, 7 were at unknown risk of bias, and 2 were at high risk of bias. The data used in the included studies included both public and non-public electronic medical record databases, and the machine learning models used included logistic regression, random forest, support vector machine, and XGBoost, etc. The predictive models constructed based on different databases appeared to have different results in terms of the characteristic variables, so identifying the key variables of the predictive models requires further validation on other datasets. Meta-analysis showed the pooled AUC of 0.812 (95%CI 0.763 to 0.860, P<0.001), and further subgroup analyses showed that larger sample sizes (≥1 000) and predictor variable types significantly improved the predictive effect of the model, and the difference in AUC was statistically significant (95%CI not overlapping). The funnel plot showed that there was publication bias in the study, and when the extreme AUC values were excluded, the meta-analysis yielded a total AUC of 0.815 (95%CI 0.769 to 0.861, P<0.001), indicating that the extreme AUC values were insensitive. ConclusionMachine learning technology has shown some potential in predicting septic shock in children, but the quality of existing research needs to be strengthened, and future research work should improve the quality of research and improve the prediction effect of the model by expanding the sample size.