This study developed a machine-learning system to automatically calculate four key radiological measurements—Reimer’s migration percentage (RMP), acetabular index (ACI), neck shaft angle (NSA), and head shaft angle (HSA)—for diagnosing and monitoring cerebral palsy-related hip disease. Evaluation on 1,650 pelvic radiographs showed excellent agreement for RMP (ICC 0.91), good agreement for NSA (ICC 0.85), and moderate agreement for ACI (ICC 0.66) and HSA (ICC 0.73), with no significant bias detected. The system is accurate enough for clinical use, offering potential time savings and improved reliability in monitoring hip migration in children with CP.