Thyroid is a gland situated at the base of the neck that has a huge contribution in the body metabolism, growth, and development. Thyroid disease is one of the progressive endocrine diseases. Thyroid disease and disorders, in human population, can range from small, harmless goiter (enlarged gland) to erratic mood swings, psychosis, and may even lead to life-threatening cancer. While goiter may or may not require treatments since the most common thyroid problems involve abnormal production of thyroid hormones. AI-based models have an important role in dealing with such disease. For classification of thyroid disease, Kaggle repository database was used. Some of the variables taken into account for this were age, sex, sickness, pregnancy, thyroid surgery, I131 treatment, lithium, goiter, tumor, TSH, T3, TT4, T4U, and FTI, etc. This chapter proposes a AI-based neural network and logistic regression models for diagnosing the thyroid disease aimed at early prediction of the patients. This approach enhances prediction accuracy, potentially reducing the adverse effects of thyroid disease. We use more than 3772 patients worth of data with 27 predictors using a random split of 70/30 for train/test using “root mean squared error” (RMSE) for performance in “decision tree” (DT). With comparative study, different AI-based models are used by the proposed system to achieve best accuracy results in disease prediction. Furthermore, using the decision tree, we found that only five risk factors are sufficient to diagnose thyroid disease without depending on the rest 22 attributes of patients with RMSE of 0.044912886. Efficiency of the diagnosis of this neurodegenerative disease is tested by “decision tree” (DT), “artificial neural network” (ANN) having hidden layer of one, ANN having two hidden layers, hybrid ANN, different “deep neural network” (DNN), hybrid DNN, logistic regression, and hybrid DT-Logistic Regression models. Hybrid DT-Logistic Regression model has shown finest result for diagnosing thyroid disease with accuracy of 0.998 and 0.997 with train and test dataset respectively which outperforms other models.