A study evaluated the use of Electroencephalography (EEG) as a diagnostic tool for attention-related disorders, including ADHD, anxiety, and learning disabilities. EEG data were collected from 31 participants, including those with ADHD, during a Go/No-Go task that tested attention and impulsivity. Researchers analyzed the spectral characteristics of brain activity, focusing on the relative power of theta, alpha, and beta frequency bands, as well as the theta-to-beta ratio (TBR). Results showed that participants with ADHD had higher theta power and elevated TBR, especially in the Frontal, Temporal, and Occipital regions. Machine learning models, particularly K-Nearest Neighbors, successfully classified ADHD and control groups based on TBR with high accuracy. Participants with ADHD had faster reaction times but made more errors on the task, indicating challenges with sustained attention. The study suggests that combining EEG with machine learning could lead to effective diagnostic tools for attention-related disorders. Despite some limitations, the findings highlight the potential for developing brain-computer interface systems to assess attention processes.