Predictive analytics in peripheral vascular catheter management may improve patient outcomes and reduce complications. The main goal is to utilise data to predict and reduce catheter hazards. Machine learning algorithms are used to detect infections, thrombosis, and catheter dislodgement early, allowing for prompt management. Predictive models provide healthcare practitioners vital information to improve catheter care bundles and enable more accurate and personalised therapy. This strategy may reduce catheter-related problems and improve patient safety, making vascular access management more proactive. Thus, predictive analytics improves peripheral vascular catheterisation clinical procedures and standards. PVCCB_Analytics_Dataset displays Predictive Model Performance Metrics 1 has F1 score, accuracy, precision, and AUC are important. Models A to C have accuracy values from 85.40 to 91.0, precision values from 80.2 to 89.1, recall values from 82.8 to 90.2, F1 scores from 81.5 to 89.6, and AUC values from 0.86 to 0.93. In Risk Stratification Distribution 2, high, low, medium risk levels, 120-250 patients, Infection incidence varies from 3.1 to 15.2, compliance rate from 70.5 to 95.2, duration from 10.2 to 20.1 days, and complications from 1.2 to 8.3. © 2025 Elsevier B.V., All rights reserved.