Background: The impact of PICC-related thrombosis is worth paying attention to, and it is important to predict the risk factors for thrombosis in patients with PICC catheterization， accurate scientific assessment tools are critical forpredictingand preventing thrombosis in patients with PICCs.The main objective is to develop and validate a machine learning model for predicting the risk of peripherally inserted central catheter-related venous thrombosis. Methods: Overall, 626 patients undergoing peripherally inserted central catheter placement from January 2016 to October 2020 were enrolled. The variables included patient demographic characteristics, clinical condition, laboratory examinations, treatment, and catheter-related factors. Support vector machine and genetic algorithm were used to develop and optimize the model, respectively. SHapley Additive exPlanations was used to interpret the model. Results: The model obtained an average area under the receiver operating characteristic curve of 0.95. The SHapley Additive exPlanations summary plot was used to illustrate the effects of the top 20 features from support vector machine. This study provides a visual way to illustrate the impact of input features on the result prediction. Conclusions: The machine learning model developed based on genetic algorithm shows good predictive ability in patients with a high risk of thrombosis-related peripherally inserted central catheter. Trial registration:retrospectively registered.