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Electroencephalography (EEG) is a non-invasive method that records and analyzes the electrical activity of the brain. An electroencephalogram (EEG) is a simple and affordable supplemental examination that can aid in the investigation and diagnosis of neurological diseases that have an impact on the brain. Accurate diagnosis of certain neurological diseases using EEG data can be challenging and time-consuming, requiring highly specialized training and expertise. Misdiagnosis can have severe consequences, leading to delayed or inappropriate treatment. In this project, we propose to develop a machine learning algorithm that can accurately detect specific neurological diseases using EEG data. Our project aims to preprocess EEG data from patients with Obsessive compulsive disorder, Addictive disorder, Trauma and stress related disorder, Healthy control, Mood and anxiety disorder, extract relevant features, label the data, train the algorithm, test and validate its performance, and finally deploy it on a web app. We will use various machine learning techniques, such as Logistic Regression ElasticNet, Random Forest, Support Vector Machines, LightGBM, CatBoost and K-Nearest Neighbors, to train the algorithm. Our ultimate goal is to create an automated and accurate method of analyzing EEG data that can aid clinicians in making accurate diagnoses and improve patient outcomes. The development of such an algorithm has the potential to significantly improve the efficiency and accuracy of neurological disease diagnosis and treatment.