Abstract: Objective To evaluate the scientificity of artificial intelligence （AI）-based localization model for venous puncture sites in contrast-enhanced imaging examination and evaluate its clinical efficacy. Methods A prospective observational study was conducted. A total of 433 patients who underwent contrast-enhanced imaging examination at Sun Yat-sen Memorial Hospital of Sun Yat-sen University and Shenshan Central Hospital of Sun Yat-sen Memorial Hospital of Sun Yat-sen University from June 25 to July 11, 2025 were collected. Data were summarized and analyzed with reference to the indicators in the standardized application of a three-level evaluation model for inspecting peripheral venous access through image enhancement. Multimodal data were collected to establish an intelligent data preprocessing framework and develop an AI-based localization model for venous puncture sites in contrast-enhanced imaging examination. Taking the puncture sites confirmed by nurses with more than 5 years of working experience in Radiology Department as the gold standard, the degree of agreement between the puncture sites recommended by the AI-based localization model and the gold standard was evaluated. Results Among 433 patients, 380 cases were allocated into the low-risk group and 53 into the high-risk group. The Dice coefficient and Intersection over Union （IoU） of the AI-based localization model were 0.593 1 and 0.496 8, respectively, with an overall accuracy of 0.967 1. In the low-risk group, the Dice coefficient and IoU were 0.617 8 and 0.506 9, the recall rate reached 0.791 2, and the MLE score was 68.07, respectively. In the high-risk group, the Dice coefficient and IoU were 0.553 1 and 0.478 2, the recall rate was 0.702 4, and the MLE score was 64.18, respectively. There were significant differences in the Dice coefficient, IoU, accuracy rate, recall rate and MLE score between the low-risk and high-risk groups （all P < 0.001）. In the age stratification analysis, the Dice coefficients in the youth, middle-aged and middle-aged groups were 0.581 0, 0.659 8 and 0.629 2, and the IoU was 0.456 3, 0.502 1 and 0.529 8, the accuracy rates exceeded 0.95 and the recall rates were 0.635 0, 0.759 1 and 0.710 4, respectively. The Dice coefficient and IoU in the elderly group were 0.550 6 and 0.524 7, the accuracy rate was 0.946 3 and recall rate was 0.670 1, respectively. There was statistical significance in each index between any of two groups （all P < 0.05）. Conclusion The AI-based localization model established in this study can effectively improve the robustness of puncture site localization, provide consistent explanations aligned with clinical intuition, and offer an efficient, safe guidance regimen for selecting venous puncture sites.