Neurodivergence is defined as people with atypical brain structures, affecting their learning patterns, thinking, information processing, and communication with others. Autism, Dyslexia, ADHD, and dyscalculia count as the main types of neurodivergence. While some of them have been the subject of study since the early years of the 20th Century. ADHD is defined as attention deficit and hyperactivity disorder. People with ADHD are more likely to struggle professionally, academically, and personally. Given that their brains function differently, they tend to be misunderstood and problematic for their surroundings. Yet, until now, very little importance has been given to mental health globally and to ADHD cases specifically. The diagnosis remains extremely complex and susceptible to mistakes, such as underdiagnosing or confusion with other diseases. Current diagnostic practices primarily rely on observed behavioural patterns, though neurological differences such as dopamine regulation and brain structure abnormalities also play a role. The current approach for ADHD diagnosis, a condition categorized as a form of neurodivergence, first from a medical perspective and then from a technological one, many AI tools can be developed to assist and enhance the diagnostic process. The main idea is to audit the extent to which artificial intelligence can reduce existing error risks in clinical methods, enhance their accuracy, and make them more accessible. The paper includes a comparative analysis of traditional diagnostic methods, focusing on clinical evaluations and standardized behavioural criteria (DSM-5, EEG neuroimaging, MRI scans, SPECT scans, etc.). It also explores AI-based solutions present in recent literature and medical research as potential tools for enhancing ADHD diagnosis. The study anticipates that AI-driven diagnostic methods may offer significant improvements related to efficiency, objectivity, and reach, particularly in underserved or resource-limited settings. However, it also expects to identify major concerns related to data bias, ethical considerations, and clinical applicability, which may limit immediate integration into practice. AI’s help could significantly elevate the ADHD diagnosis methods. It is undeniable that clinical methods and specialized practitioners’ help will always be mandatory and necessary. However, human error and bias could always occur and be a challenge that requires recurrent examination. Therefore, the use of AI is extremely efficient while relying on a psychiatrist/neurologist. Such a hybrid approach balances technological innovation with human oversight.