Introduction: The early identification and prediction of intravenous immunoglobulin (IVIG)- resistant Kawasaki disease (KD) patients remain essential for mitigating the incidence of coronary artery complications. Objectives: This research aimed to construct and validate an interpretable prediction model for IVIG-resistant KD using machine learning (ML) approaches, convert it into an intuitive software application and explore the pathogenic contributors to IVIG-resistant in KD. Methods: Twelve ML algorithms were developed using 33 clinical variables (available within 24h) from 3,023 KD patients at Children’s Hospital of Soochow University (2020–2024). Models were evaluated via AUC and interpreted using SHAP. External validation was performed on 1,626 patients from Anhui Provincial Children's Hospital, Affiliated Xuzhou Children's Hospital, and Jiangsu Province (Suqian) Hospital. Results: Among the twelve ML algorithms, the extra trees (ET) model exhibited superior discriminative capacity . Upon dimensionality reduction based on the ranked importance of features, an interpretable ET model incorporating eight variables was finalized. It achieved AUC values of 0.865 in internal validation, 0.810 in the Anhui cohort (n = 654), 0.744 in the Xuzhou cohort (n =546), and 0.785 in the Suqian cohort (n=408). Furthermore, the model has been implemented as an accessible web-based application, enhancing its feasibility in clinical settings. Conclusions: This investigation established a reliable and interpretable ML model capable of predicting IVIG-resistant KD with high accuracy . The interpretation of machine learning techniques by adopting the SHAP method has potential value in helping to start preventive intervention for children with IVIG-resistant KD patients at an early stage.