This short report discusses the use of Convolutional Neural Networks (CNNs) in detecting Attention Deficit Hyperactivity Disorder (ADHD). ADHD is characterized by abnormalities in brain structure and function, particularly in the prefrontal cortex, and is associated with neurochemical imbalances and genetic factors. Traditional diagnostic methods often rely on subjective evaluations and standardized surveys, leading to inconsistencies and incorrect diagnoses. CNNs, a form of deep learning, excel in image recognition tasks and can be applied to analyze brain-imaging data such as functional MRI scans. By learning hierarchical features from raw pixel values, CNNs can automatically detect relevant brain structures and abnormalities associated with ADHD. The report highlights several studies that demonstrate the effectiveness of CNNs in diagnosing ADHD using various data sources, including EEG signals, fMRI data, and ECG signals. These studies achieve high accuracy rates, ranging from 88% to 98.77%, and provide insights into specific brain regions and frequency bands that are significant for higher-order neurocognitive processes. The report also discusses the potential of advanced CNN techniques, such as 3D CNNs and transfer learning, to enhance diagnostic accuracy and interpretability. Future research directions include combining neuroimaging with behavioral data, incorporating EEG and ECG data, and further exploring the use of 3D CNNs for volumetric neuroimaging data. The report concludes that CNNs offer a paradigm shift in ADHD care, providing more precise, personalized, and effective treatments, and promising significant improvements in patient quality of life.This short report discusses the use of Convolutional Neural Networks (CNNs) in detecting Attention Deficit Hyperactivity Disorder (ADHD). ADHD is characterized by abnormalities in brain structure and function, particularly in the prefrontal cortex, and is associated with neurochemical imbalances and genetic factors. Traditional diagnostic methods often rely on subjective evaluations and standardized surveys, leading to inconsistencies and incorrect diagnoses. CNNs, a form of deep learning, excel in image recognition tasks and can be applied to analyze brain-imaging data such as functional MRI scans. By learning hierarchical features from raw pixel values, CNNs can automatically detect relevant brain structures and abnormalities associated with ADHD. The report highlights several studies that demonstrate the effectiveness of CNNs in diagnosing ADHD using various data sources, including EEG signals, fMRI data, and ECG signals. These studies achieve high accuracy rates, ranging from 88% to 98.77%, and provide insights into specific brain regions and frequency bands that are significant for higher-order neurocognitive processes. The report also discusses the potential of advanced CNN techniques, such as 3D CNNs and transfer learning, to enhance diagnostic accuracy and interpretability. Future research directions include combining neuroimaging with behavioral data, incorporating EEG and ECG data, and further exploring the use of 3D CNNs for volumetric neuroimaging data. The report concludes that CNNs offer a paradigm shift in ADHD care, providing more precise, personalized, and effective treatments, and promising significant improvements in patient quality of life.