Attention deficit hyperactivity disorder (ADHD) is the most common developmental disease in childhood and adolescence. Most similar previous ADHD classification studies have only classified ADHD and normal categories. This study was the first attempt to classify adults with ADHD by means of a support vector machine indicating that classification by nonlinear methods is possible in the context of clinical populations.  Some of the guidelines were used in this systematic review to analyze the studies most relevant to the diagnosis of ADHD using the Deep learning (DL) ​​approach. This paper also attempted to look at the process of ADHD development; what are the associated problems? And how many other children and adults are affected by such problems around the world the basis for understanding ADHD more accurately in order to develop a better multi-modal medical and/or non-medical intervention plan and some classification technique of ADHD will be presented. The diagnosis of it includes conducting some psychological tests, but it may not give accurate results, so the diagnosis is made using non-invasive imaging, which is called Magnetic Resonance Imaging (MRI). For this purpose and according to the medical imaging system, first, the images are pre-processed, and then 3D images are taken and this is done using the ADHD-200 training data set. This shows the best results and classification with high accuracy