Early detection of speech disorders in children is critical for effective intervention and treatment. This paper presents an innovative tool leveraging the Internet of Things (IoT) and advanced data mining techniques to identify speech disorders in children at an early stage. This paper explores the integration of IoT and data mining technologies for the early detection of speech disorders in children by applying advanced algorithms. The aim is to develop an efficient tool for early diagnosis and intervention, enhancing speech therapy outcomes through real-time monitoring and analysis. The system integrates IoT-enabled devices for real-time data collection, capturing various speech parameters such as pitch, tone, and articulation. These data are processed using advanced algorithms, including machine learning and deep learning techniques, to analyze patterns and anomalies indicative of speech disorders. The proposed paper offers a user-friendly interface for parents, educators, and healthcare professionals, providing insights into a child′s speech development and highlighting potential areas of concern. The system′s effectiveness is evaluated through extensive testing with a diverse dataset, demonstrating its accuracy and reliability in identifying various speech disorders, such as stuttering, dysarthria, and apraxia. By facilitating early diagnosis, this tool aims to improve the quality of life for children with speech disorders through timely and targeted interventions.