Developmental Dysplasia of the Hip (DDH) is a prevalent pediatric condition requiring early detection to prevent severe long-term consequences. This review explores the application of machine learning (ML) technologies to improve the accuracy and reliability of DDH diagnostics, which traditionally rely on subjective methods. Synthesizing f indings from 45 studies published between 2016 and 2024, the review highlights the methodologies, outcomes, and clinical implications of ML-driven solutions, including deep learning models, automated imaging systems, and hybrid frameworks. These innovations demonstrate significant potential in reducing diagnostic variability, enhancing early detection, and improving treatment outcomes for infants. However, challenges such as dataset diversity and clinical validation persist. This comprehensive overview emphasizes the transformative impact of ML on pediatric orthopedics and outlines future research directions for broader implementation.