Developmental dysplasia of the hip (DDH) poses significant challenges in both childhood and adulthood, affecting up to 10 per 1000 live births in the United Kingdom and United States. While newborn screening aims to detect DDH early, missed diagnoses can lead to severe complications such as hip dysplasia and early onset osteoarthritis in adults. Treatment options range from less invasive procedures like hip-preserving surgery to more extensive interventions such as total hip arthroplasty (THA), depending on the severity of the condition. Preoperative planning plays a critical role in optimizing surgical outcomes for DDH patients undergoing THA. This includes accurate imaging modalities, precise measurement of acetabular bone stock, assessment of femoral head subluxation, and predicting prosthesis size and leg length discrepancy. Recent advancements artificial intelligence and machine learning offer promising tools to enhance preoperative planning accuracy. However, challenges remain in validating these technologies and integrating them into clinical practice. This editorial highlights the importance of ongoing research to refine preoperative strategies and improve outcomes in DDH management through evidence-based approaches and technological innovations.