Thyroid disorders, including hypothyroidism and hyperthyroidism, are among the prevalent endocrine conditions that impact people’s health worldwide, and a diagnosis must be made correctly and as soon as possible for treatment to be effective. Using a dataset of 3772 records with 30 features, this study aims to investigate machine learning classifiers including decision tree, gradient boosting, random forest, SVM, Naive Bayes, XGBoost, KNN, AdaBoost, and logistic regression. Employing feature selection techniques like recursive feature elimination, sequential forward selection, and univariate selection; the key predictors were identified; and of these, TSH was the most important. Furthermore, other feature importance measures were employed, such as permutation importance, XGBoost importance, and random forest importance. Accuracy was lowest for Naive Bayes (63.76%) and highest for decision tree (99.38%). The benefit of incorporating machine learning techniques into clinical practice for the diagnosis of thyroid issues was proved by the excellent performance from ensemble models like gradient boosting and random forest.