Alcoholism is a pervasive public health issue with far-reaching consequences for individuals and society as a whole. The neurological effects of chronic alcohol use have long been a subject of scientific inquiry. This paper explores the intricate world of EEG (Electroencephalogram) data analysis as a powerful means to unravel the neurological complexities of alcoholism. Through a comprehensive examination of EEG signal processing techniques, including bandpass filtering, ICA and correlation analysis, we aim to unearth valuable insights from EEG data collected from individuals with alcohol use disorders. Furthermore, in this study, various supervised machine learning algorithms, such as Logistic Regression, AdaBoost, Random Forest and Support Vector Classifier (SVC), were utilized to classify EEG data and distinguish between alcoholics and non-alcoholic control groups. A CNN model was also developed which yielded the best scores amongst all the algorithms. These models allowed us to predict the accuracy of differentiation between the groups, contributing to our understanding of the neurobiological basis of alcoholism.