This study investigated the use of artificial intelligence (AI) and machine learning (ML) to classify pediatric uroflowmetry curves with the goal of reducing variability and improving diagnostic accuracy in bladder-bowel dysfunction. A total of 586 standardized uroflowmetry curves from children aged 5–17 were analyzed and classified into bell, tower, plateau, staccato, or interrupted patterns by three pediatric urologists, achieving high inter-rater agreement (Fleiss’ kappa = 0.948). The YOLOv5×6 algorithm was trained on 85% of the dataset and validated on the remaining 15%, with performance assessed by accuracy, precision, recall, F1-score, and mean Average Precision (mAP). The AI model achieved an overall accuracy of 85.8%, with strong performance in identifying bell-shaped curves (96% success) and perfect precision for plateau patterns, though staccato patterns were less reliably classified (precision 0.64). The model’s learning stabilized at around 90% mAP after 50 training epochs. Findings highlight that curve shape is particularly important in pediatric assessment, but traditional interpretation is limited by subjectivity and inconsistent scaling. The authors concluded that AI-based curve classification is feasible and effective, and future work should focus on multicenter datasets, standardized scaling, and international collaboration to enhance clinical applicability.