In the modern world Machine learning plays a key role in the field of medical science, particularly in diagnosing health conditions and providing appropriate treatment at early stages. In the case of thyroid disease, traditional diagnostic methods involve detailed inspections and various blood tests. The primary aim and objective is to detect the syndrome of thyroid at initial stages with high level of accuracy. Machine Learning (ML) methods significantly enhance medical decision-making, accurate diagnosis, and reduce patient costs and time. This study aims to predict thyroid disease using dissimilar machine learning models, employing Random Forest, Support Vector Machine (SVM) and Logistic Regression algorithms. Thyroid patient dataset with relevant attributes is used to test these algorithms' effectiveness in diagnosing the disease. The outcomes determine the prospective of these machine learning methods in improving early diagnosis and treatment outcomes for thyroid patients.