This study focuses on Attention Deficit Hyperactivity Disorder (ADHD), a neurodevelopmental disorder that affects both children and adults. Individuals with ADHD often struggle with difficulties related to attention, impulse control, and hyperactivity. To learn more about ADHD, researchers have employed a variety of neuroimaging modalities and analysis techniques over the years. To research brain activity in children with ADHD, this study examines the characteristics of Electroencephalogram (EEG) data using Machine Learning Techniques, which can be a trustworthy diagnostic tool for physicians. After analyzing the EEG data obtained, we can infer from this empirical investigation that the frontal regions of the brain are mostly active and model accuracy is 80% for ADHD classification.