This study aimed to help healthy adults achieve self-screening by analyzing the quantitative relationship between body composition index measurements (BMI, waist-to-hip ratio, etc.) and dyslipidemia and establishing a logical risk prediction model for dyslipidemia. We performed a cross-sectional study and collected relevant data from 1115 adults between November 2019 and August 2020. The least absolute shrinkage selection operator (LASSO) regression analysis was performed to select the best predictor variables, and multivariate logistic regression analysis was used to construct the prediction model. In this study, a graphic tool including 10 predictor variables (a "nomogram," see the precise definition in the text) was constructed to predict the risk of dyslipidemia in healthy adults. A calibration diagram, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used to verify the model’s utility. Our proposed dyslipidemia nomogram showed good discriminative ability with a C-index of 0.737 (95% confidence interval, 0.70–0.773). In the internal validation, a high C-index value of 0.718 was achieved. DCA showed a dyslipidemia threshold probability of 2–45%, proving the value of the nomogram for clinical application for dyslipidemia. This nomogram may be useful for self-screening the risk of dyslipidemia in healthy adults.