Attention Deficit Hyperactivity Disorder (ADHD), is a neurodevelopmental disorder characterized by hyperactivity, inattention and impulsivity, which has an impact on the cognitive and behavioral process. Early and accurate diagnosis is required for preventing long-term academic, emotional and social impairments. The traditional diagnosis techniques are based on subjective behavioral evaluation, which is prone to misinterpretation. To address this issue, this study suggests a hybrid machine learning model, which uses a Voting Classifier combining Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) for improving the diagnostic accuracy of ADHD. Electroencephalography (EEG) data from the ADHD – 200 datasets of the Kennedy Krieger Institute is used in this study. To optimize the performance of the model, the dataset is subjected to detailed processing, feature selection and dimensionality reduction. This hybrid model uses a Voting Classifier for combining the benefits of SVM for dealing with high dimensional data and KNN in detecting localized data patterns. This model outperformed individual classifiers with an impressive 96% classification accuracy. Using this hybrid model, we achieved an increase in F1 score, precision, recall and accuracy. This study shows the efficiency of hybrid machine learning model using the EEG data in detecting the ADHD.