A recent randomised controlled trial failed to demonstrate a beneficial effect of recombinant human thrombomodulin (rhTM) on sepsis. However, there is still controversy in the effects of rhTM for sepsis due to the heterogeneity of the study population. We previously identified patients with a distinct phenotype that could be a potential target of rhTM therapy (rhTM target phenotype). However, for application in the clinical setting, a simple tool for determining this target is necessary. Thus, using three multicentre sepsis registries, we aimed to develop and validate a machine learning model for predicting presence of the target phenotype that we previously identified for targeted rhTM therapy. The predictors were platelet count, PT-INR, fibrinogen, fibrinogen/fibrin degradation products, and D-dimer. We also implemented the model as a web-based application. Two of the three registries were used for model development (n = 3694), and the remaining registry was used for validation (n = 1184). Approximately 8-9% of patients had the rhTM target phenotype in each cohort. In the validation, the C statistic of the developed model for predicting the rhTM target phenotype was 0.996 (95% CI 0.993-0.998), with a sensitivity of 0.991 and a specificity of 0.967. Among patients who were predicted to have the potential target phenotype (predicted target patients) in the validation cohort (n = 142), rhTM use was associated with a lower in-hospital mortality (adjusted risk difference, - 31.3% [- 53.5 to - 9.1%]). The developed model was able to accurately predict the rhTM target phenotype. The model, which is available as a web-based application, could profoundly benefit clinicians and researchers investigating the heterogeneity in the treatment effects of rhTM and its mechanisms.