Autism Spectrum Disorder (ASD) and hyperactivity impact the behavior and development of millions of children worldwide. One of the main problems in diagnosing ASD is the significant variation in symptoms among individuals. Detecting ASD is also challenging for healthcare professionals as its signs often overlap with other developmental disabilities. Traditional diagnostic procedures are based on hypothetical assessments, which are not always accurate. Enhancing children’s emotional, social, and intellectual growth calls for early diagnosis followed by timely intervention. Large volumes of data can be processed by machine learning, which can also reveal subtle patterns that traditional approaches might easily miss. The current study proposes a hybrid model that integrates LightGBM and TabNet with a high accuracy rate of 98%. The model improves diagnostic precision and aids in the creation of individualized treatment plans. The model can also be applied to children with ADHD, ASD, hyperactivity, and normal development to enable early intervention and improve growth and development. Through high-level machine learning methods, this model is beneficial to use in making more precise diagnoses, translating into better outcomes for suffering children.