Purpose　To　develop　a　predictive　model　for　orthokeratology　(Ortho-K)　lens　decentration　1　month　after　wear.Methods　This　study　included　myopic　children　who　were　fitted　with　Ortho-K　lenses　at　Fujian　Provincial　Hospital　between　December　2022　and　May　2024.　Corneal　topography　parameters　and　other　relevant　metrics　were　collected　pre-　and　post-treatment.　Feature　selection　was　conducted　using　univariate　logistic　regression　and　Lasso　regression　analysis.　A　machine　learning　approach　was　used　to　develop　multiple　predictive　models,　including　Decision　Tree,　Logistic　Regression,　Multilayer　Perceptron,　Random　Forest,　and　Support　Vector　Machine.　Model　performance　was　evaluated　using　accuracy,　sensitivity,　specificity,　ROC　curves,　DCA　curves,　and　calibration　curves.　SHAP　values　were　employed　to　interpret　the　models.Results　The　Logistic　Regression　model　demonstrated　the　best　predictive　performance,　with　an　AUC　of　0.82　(95%　CI:　0.69-0.95),　accuracy　of　77.59%,　sensitivity　of　85%,　and　specificity　of　61.11%.　The　most　significant　predictors　identified　were　age,　8　mm　sag　height　difference,　5　mm　Kx1,　and　7　mm　Kx2.　SHAP　analysis　confirmed　the　importance　of　these　features,　particularly　the　8　mm　sag　height　difference.Conclusions　The　Logistic　Regression　model　successfully　predicted　the　risk　of　Ortho-K　lens　decentration　using　key　corneal　morphological　metrics　and　age.　This　model　provides　valuable　support　for　clinicians　in　optimizing　Ortho-K　lens　fitting　strategies,　potentially　reducing　the　risk　of　adverse　outcomes　and　improving　the　quality　of　vision　for　patients.　Further　validation　in　clinical　settings　is　recommended.