Congenital heart disease affects approximately 1% of children worldwide, with a number of cases in resource-limited settings remaining undiagnosed through school age. While cardiac auscultation is a key screening method, its effectiveness varies widely, depending on practitioner expertise. This study introduces an innovative artificial intelligence (AI) approach combining conventional machine learning and deep learning techniques to improve heart murmur detection in pediatric populations. By developing an integrated Residual-Recurrent Neural Networks model and analyzing heart sound recordings from 500 pediatric participants, we achieved remarkable diagnostic performance in real-world pediatric clinical settings. At the single recording-level, the model achieved an accuracy of 88.5%, sensitivity of 85.5%, and specificity of 90.7%. Performance improved at the participant-level, with an accuracy of 90.0%, sensitivity of 88.8%, and specificity of 91.2%. The model showed particularly strong results when tested against the PhysioNet database (accuracy 95.2%, sensitivity 91.6%, and specificity 99.1%). This research provides a compelling proof-of-concept for AI-assisted cardiac screening, potentially revolutionizing early detection strategies in pediatric cardiac diseases.