The sleep disorders significantly affect quality of life and overall health. Accurately diagnosing and classifying these disorders is crucial for effective treatment. This study explores the use of machine learning techniques, specifically Random Forest, Logistic Regression, and Support Vector Machine (SVM) algorithms, to classify sleep disorders using the Sleep Health and Lifestyle dataset from Kaggle. The dataset includes information on sleep duration, quality, physical activity, stress, and other lifestyle factors. The models are evaluated for accuracy, precision, recall, and F1-score. The Random Forest model performed best with an accuracy of 91.67%. However, further refinement is needed for classes such as insomnia and sleep apnea, which show lower recall scores.