Thyroid Gland is a part of the endocrine system, it produces hormones that regulate key body functions and metabolic processes. Increased or decreased thyroid hormone results indicate that there is an imbalance between the body's requirements and supply. About 306 patient's data for training and 116 for testing are collected from Al-Jamhoree Hospital under the supervision of doctor to diagnosis the thyroid gland patients. Combining the Fuzzy Logic approach with ANN concept this study was conducted. In this paper there are two purpose; first; investigation the applicability and capability of the three proposed algorithms Fuzzy Radial Base Function neural networks: FuzRBF-1, 2, 3 for detect and diagnosis thyroid disease. The fuzzy logic is used to design fuzzy expert system Fuzzy Thyroid Gland FIS that deals with uncertain risk factors; this is the first step of these algorithms. In the second step, three methods of RBF neural network (RBF-1, 2, 3) are applied to deal with others factors (sign and symptoms of the thyroid disease) as well as the output of Fuzzy Thyroid Gland FIS after been normalized. Experimental results show that the overall performance of FuzRBF-1 algorithm is better than those of the others which gives DR= 100% for training and testing thyroid data set with 0.0468 and 0.0156 second execution time respectively. The second purpose of this study: an intelligent system The Thyroid's Patients Diagnosis System for the automatic diagnosis of the thyroid gland disease has been designed that outperform the traditional systems. The FuzRBF-1 algorithm has been applied in this system as well as thyroid database has been established that include the detailed information about patients and providing the report of each patient which is implemented using GUI in MATLAB 7.10. 0 (R2010a) language. The diagnosis performance of The Thyroid's Patients Diagnosis System shows the advantage of this intelligent system: it is rapid, easy to operate, noninvasive and not expensive. It also helps for training beginner’s doctors and medical students who work in the tedious and complicated task of diagnosing thyroid diseases.