Machine learning is an effective method for making predictions and diagnosing thyroid disease by analyzing large patient datasets to identify patterns and correlations. It can improve disease diagnosis accuracy and identify patients at risk. The paper focuses on machine learning’s use to analyze and classify thyroid disease using Aadesh Medical College and Hospital data, encompassing demographics of patients, histories of illness, and findings from the lab. This analysis can train machine learning models to predict disease onset and severity and create personalized treatment plans. The dataset we have used includes patient demographics, thyroid profile (T3 and TSH levels), complete blood count (CBC) results, and comorbidities (such as diabetes, hypertension, and cardiovascular disease). Among the approaches we employ are decision tree optimization, frontier boosting, logistical regression estimation, support vector machine, and linear regression (LR). In order to enhance the models’ training, we have also used a number of hyperparameters, including C, “break_ties,” “cache_size,” “class_weight,” “coef0,” “degree,” “gamma,” “kernel,” “max_iter,” “probability,” “random_state,” “shrinking,” “tol,” and "verbose." The decision tree (regressor) model's accuracy of 97.53% produces the best results, and another standout aspect of our work is the achievement of over 96.29% accuracy levels for the linear regression models. Other research studies may have used these methods, but their reported accuracies for these two models do not match ours.