Objective To investigate the high-risk factors for preeclampsia in Urumqi, establish a risk prediction model, and validate its effectiveness. Methods A total of 6 138 pregnant women undergoing antenatal examination from February 2021 to February 2023 were collected through the integrated platform of clinical research in Urumqi Maternal and Child Health Hospital. General data, pregnancy history, and pregnancy complications were recorded. The patients were randomly divided into a modeling group (n=4 308) and a validation group (n=1 830) in a 7:3 ratio. The logistic regression model analysis method was used to construct a Nomogram prediction model. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were employed to assess the discrimination, calibration, and clinical applicability of the prediction model. Results The advanced age (OR=1.887, 95%CI: 1.535-2.319), overweight (OR=3.221, 95%CI: 2.699-3.844), family history (OR=1.575, 95%CI: 1.254-1.979), hypertension history (OR=17.552, 95%CI: 7.021-43.882), hypertension complicating pregnancy (OR=1.827, 95%CI: 1.388-2.405), preeclampsia history (OR=6.216, 95%CI: 3.578-10.770), and primiparity (OR=2.384, 95%CI:1.777-3.200) were identified as independent risk factors of preeclampsia in Urumqi. The results of ROC curve test showed that the area under the curve and 95%CI of the model group were 0.762 (0.742-0.782), and for the validation group were 0.765 (0.734-0.796). The calibration curve and DCA curve demonstrated that the Nomogram had good accuracy and clinical applicability. Conclusion The established nomogram prediction model exhibited good predictive ability and provides an important reference for clinical practitioners to identify high-risk patients. Early intervention measures should be implemented to prevent further progression and improve maternal and infant outcomes.