Hypothyroidism, a prevalent chronic health condition, can lead to serious complications if untreated. Management typically involves synthetic thyroid hormone replacement, with dosage being crucial for effective treatment. However, factors like stress and weight fluctuations impact thyroid hormone levels, posing challenges in dosage determination. This study introduces an innovative approach using machine learning for precise dosage prediction. We developed a synthetic thyroid disease dataset, encompassing parameters such as age, gender, TSH, T3, and T4, to train and evaluate various machine learning models. The study aimed to surpass the current state-of-the-art in dosage prediction, which is Poisson Regression with a 64.8% accuracy. Our findings reveal that Ridge Regression and Lasso Regression achieved an accuracy of 82%, while Support Vector Regression Machines attained 83%. Notably, k-Nearest Neighbour (k-NN) algorithm demonstrated the highest accuracy of 86%, marking a significant improvement of over 21% from the existing standard. This enhancement in prediction accuracy holds potential for optimizing treatment efficacy and patient outcomes in hypothyroidism management.