Neurodevelopmental disorders, including autism spectrum disorder (ASD), attention deficit/hyperactivity disorder (ADHD), and epilepsy, significantly impact cognitive, social, and motor development. Early detection and personalized intervention are critical for improving patient outcomes, yet traditional diagnostic methods often rely on subjective assessments, leading to delayed diagnosis and intervention. This study examines different feature extraction methods for ADHD, ASD, and epilepsy, explores existing solutions, and compares their effectiveness. By leveraging advanced techniques in statistical analysis, machine learning, and deep learning, this study seeks to provide more objective and timely diagnostic tools for these disorders.