This study explores the application 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 influenced by neurochemical imbalances and genetic factors. Traditional diagnostic methods often rely on subjective evaluations, leading to inconsistencies. CNNs offer a data-driven approach that enhances diagnostic precision and clinical decision-making. CNNs excel at image recognition tasks and can analyze brain imaging data such as MRI and fMRI scans. They automatically detect relevant brain structures and abnormalities associated with ADHD by learning hierarchical features from raw pixel values. Advanced CNN techniques, such as 3D CNN, are particularly effective for volumetric data like MRI. Transfer learning further enhances CNN performance by leveraging pre-trained models on large image datasets. Several studies demonstrate the effectiveness of CNNs in ADHD detection. For example, a study using EEG signals achieved 94.52% accuracy, while another using fMRI data reported up to 98.77% accuracy. The Frequency-Integrated Visual-Language Network (FIVLNet) and other CNN-based models have shown high accuracy in diagnosing ADHD using MRI scans. These models incorporate techniques like Grad-CAM to provide interpretable visualizations, aiding in understanding and trusting the results. The study also highlights the potential of CNNs in predicting treatment outcomes and personalizing treatment plans for ADHD patients. Future research directions include the development of explainable AI models, integration of real-time monitoring tools, and expansion of collaborative networks for data exchange and validation. Ethical frameworks and regulatory requirements are essential to ensure the responsible use of deep learning technology in clinical settings. In conclusion, CNNs represent a paradigm shift in ADHD care, offering more precise, personalized, and effective treatments. The integration of multimodal data and the development of explainable AI models are crucial for advancing the field of neuropsychiatric research. Continuous research promises significant improvements in patient quality of life.This study explores the application 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 influenced by neurochemical imbalances and genetic factors. Traditional diagnostic methods often rely on subjective evaluations, leading to inconsistencies. CNNs offer a data-driven approach that enhances diagnostic precision and clinical decision-making. CNNs excel at image recognition tasks and can analyze brain imaging data such as MRI and fMRI scans. They automatically detect relevant brain structures and abnormalities associated with ADHD by learning hierarchical features from raw pixel values. Advanced CNN techniques, such as 3D CNN, are particularly effective for volumetric data like MRI. Transfer learning further enhances CNN performance by leveraging pre-trained models on large image datasets. Several studies demonstrate the effectiveness of CNNs in ADHD detection. For example, a study using EEG signals achieved 94.52% accuracy, while another using fMRI data reported up to 98.77% accuracy. The Frequency-Integrated Visual-Language Network (FIVLNet) and other CNN-based models have shown high accuracy in diagnosing ADHD using MRI scans. These models incorporate techniques like Grad-CAM to provide interpretable visualizations, aiding in understanding and trusting the results. The study also highlights the potential of CNNs in predicting treatment outcomes and personalizing treatment plans for ADHD patients. Future research directions include the development of explainable AI models, integration of real-time monitoring tools, and expansion of collaborative networks for data exchange and validation. Ethical frameworks and regulatory requirements are essential to ensure the responsible use of deep learning technology in clinical settings. In conclusion, CNNs represent a paradigm shift in ADHD care, offering more precise, personalized, and effective treatments. The integration of multimodal data and the development of explainable AI models are crucial for advancing the field of neuropsychiatric research. Continuous research promises significant improvements in patient quality of life.