Background <p>This study aimed to develop and validate a treatment non-adherence risk nomogram for patients receiving intense pulsed light (IPL) therapy, utilizing internal bootstrap validation and external independent set validation.</p> Patients and methods <p>We analyzed data from 316 patients who received IPL therapy between January and December 2021. The dataset was divided into a training set (<i>n</i> = 213, 67.41%) for model development and a validation set (<i>n</i> = 103, 32.59%). Twenty-two variables spanning demographics, clinical consultations/treatments, and personal habits were evaluated. A multivariable logistic regression model was constructed using predictors selected via Least Absolute Shrinkage and Selection Operator (LASSO) regression. Model performance was assessed by discrimination (C-index), calibration (calibration curves), and clinical utility (decision curve analysis, DCA). Internal validation employed 1,000 bootstrap resamples, followed by external validation.</p> Results <p>LASSO regression identified seven predictors: number of jewels, local residents, unrealistic demands, undergone plastic surgery, regular physical exercise, awareness of sun protection, and carbonated/sugary drinks. The nomogram demonstrated strong discrimination (training C-index: 0.922, 95% CI: 0.888–0.956; modified C-index: 0.896) and excellent calibration. External validation confirmed robustness (C-index: 0.897, 95% CI: 0.832–0.962). DCA revealed superior net benefit at threshold probabilities of 1–100% (training) and 1–88% (validation), supporting clinical utility.</p> Conclusion <p>This validated nomogram provides a practical tool for individualized prediction of IPL therapy non-adherence risk, facilitating targeted interventions to improve treatment adherence.</p>