Background. We aimed to develop a predictive difficult caudal epidural blockade (pDCEB) model when ultrasound was not available and verified the role of ultrasound in difficult caudal epidural blockade (CEB). Methods. From October 2018 to March 2019, this study consisted of three phases. First, we prospectively enrolled 202 patients scheduled to undergo caudal epidural anesthesia and assessed risk factors by binary logistic regression to develop the predictive scoring system. Second, we enrolled 87 patients to validate it. The receiver operating characteristic (ROC) curve was used to evaluate the performance of the prediction model. Youden-index was used to determine the cut-off value. Third, we enrolled 68 patients with a high risk of difficult CEB (pDCEB score ≥3) and randomized them into ultrasound and landmark groups to verify the role of ultrasound. Result. The rate of difficult CEB was 14.98% overall 289 patients. We found a correlation between unclear palpation of the sacral hiatus (OR 9.688) and cornua (OR 4.725), the number of the sacral hiatus by palpation ≥1 (OR 4.451), and history of difficult CEB (OR 39.282) with a higher possibility of difficult CEB. The area under the receiver operating characteristic curve of the pDCEB model involving the aforementioned factors was 0.889 (95% CI, 0.827–0.952) in the development cohort and 0.862 (95% CI, 0.747–0.977) in the validation cohort. For patients with a pDCEB score ≥3, a preprocedure ultrasound scan could reduce the incidence of difficult CEB (55.56% in the Landmark group vs. 9.38% in the ultrasound group, p < 0.001 ). Conclusion. This novel pDCEB score, which takes into account palpation of the sacral hiatus/cornua, number of the sacral hiatus by palpation ≥1, and history of difficult CEB, showed a good predictive ability of difficult CEB. The findings suggested that performing an ultrasound scan is essential for patients with a pDCEB score ≥3. Trial registration: No: ChiCTR1800018871, Site URL: https://www.chictr.org.cn/edit.aspx?pid=31875&htm=4; Principal investigator: Jialian Zhao, Date of registration: 2018.10.14.