Eating disorders are mental health conditions characterized by abnormal eating behaviors and significant distress related to body shape or weight. The most prevalent ones include Anorexia Nervosa, Bulimia Nervosa, and Binge Eating Disorder (BED). These can lead to considerable physical and psychological harm, highlighting the importance of early intervention for effective treatment. This paper discusses the identification and classification of eating disorders by making use of machine learning and deep learning methodologies on data from Reddit, a social media platform. The research explores the effectiveness of several classification techniques to accurately detect and classify eating disorders. This study employs a system for detecting eating disorders that utilizes the PRAW API to gather data from Reddit. The system first performs binary classification, which determines the presence of an eating disorder, followed by multi-class classification, which classifies it into one of the four types: Anorexia Nervosa, Bulimia Nervosa, Binge Eating Disorder, and Pica. The models used are XGBoost, Random Forest (RF), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), Gated Recurrent Unit (GRU), and Bidirectional Gated Recurrent Unit (BiGRU). The above approach can be deployed as a web application that requests a brief narrative from the user before using the model to determine whether an eating disorder is present and if so, what kind it is. Additionally, it offers some exercises (Asanas/Yoga poses) for the predicted type.