Background:　This　study　aimed　to　establish　and　validate　machine　learning　(ML)　models　to　predict　the　prognosis　of　regenerative　endodontic　procedures　(REPs)　clinically,　assisting　clinicians　in　decision-making　and　avoiding　treatment　failure.　Methods:　A　total　of　198　patients　with　268　teeth　were　included　for　radiographic　examination　and　measurement.　Five　Machine　Learning　(ML)　models,　including　Random　forest　(RF),　gradient　boosting　machine　(GBM),　extreme　gradient　boosting　(XGB),　Logistic　regression　(logR)　and　support　vector　machine　(SVM)　are　implemented　for　the　prediction　on　two　datasets　of　follow-up　periods　of　1-year　and　2-year,　respectively.　Using　a　stratified　five　folds　of　cross-validation　method,　each　dataset　is　randomly　divided　into　a　training　set　and　test　set　in　a　ratio　of　8　:　2.　Correlation　analysis　and　importance　ranking　were　performed　for　feature　extraction.　Seven　performance　metrics　including　area　under　curve　(AUC),　accuracy,　F1-score,　sensitivity,　specificity,　positive　predictive　value　(PPV)　and　negative　predictive　value　(NPV)　were　calculated　to　compare　the　predictive　performance.　Results:　The　RF　(Accuracy　=　0.91,　AUC　=　0.94;　Accuracy　=　0.84,　AUC　=　0.86)　and　GBM　(Accuracy　=　0.91,　AUC　=　0.93;　Accuracy　=　0.84,　AUC　=　0.85)　had　the　best　and　similar　performance　simultaneously　in　the　prediction　of　1-year　follow-up　period　and　2-year　follow-up　period,　respectively.　The　variables　applied　to　predict　the　primary　outcome　in　REPs　were　ranked　accordingly　to　their　values　of　feature　importance,　including　age,　sex,　etiology,　the　number　of　root　canals,　trauma　type,　swelling　or　sinus　tract,　periapical　lesion　size,　root　development　stage,　pre-operative　root　resorption,　medicaments,　scaffold,　second　REPs,　previous　root　canal　filling.　Conclusions:　RF　and　GBM　models　outperformed　XGB,　logR,　SVM　models　on　the　overall　performance　on　our　datasets,　exhibiting　the　potential　capability　to　predict　the　prognosis　of　REPs.　The　ranking　of　feature　importance　contributes　to　establishing　the　scoring　system　for　prognosis　prediction　in　REPs,　assisting　clinicians　in　decision-making.　©　The　Author(s)　2025.