The majority of conventional alcohol detection approaches are based on traditional machine learning approaches and are unable to retrieve the deeply hidden attributes of alcohol-based electroencephalography (EEG) signals. The intention of this research is to evolve a deep learning-based method for detecting alcohol-related EEG signals automatically. It also investigates whether a deep learning method for alcoholism classification is effective without using any explicit feature extraction and selection steps. To assess this, the paper implements and compares deep learning-based strategies to classify alcohol-related EEG signals. A framework that uses prerecorded raw EEG data as input to a deep learning algorithm called convolutional neural network (CNN), long short-term memory (LSTM), and a cascade model called CNN + LSTM is also implemented for detecting alcoholism on an openly accessible UCI Alcoholic EEG dataset. Some conventional techniques are also implemented on prerecorded raw EEG signals, and the classification results are compared with the proposed methods. The results of the experiments show that the proposed models are capable of achieving average classification accuracies of 99.60%, 98.12%, and 95.95% on the training dataset and 92.77%, 89%, and 91% on the testing dataset along with error rates of 7.5%, 11.90%, and 8.7% from CNN, LSTM, and CNN + LSTM, respectively.