Medical image analysis plays a crucial role in the early detection and diagnosis of thyroid nodules, which are indicative of various thyroid illnesses. Thyroid nodules are classified using machine learning methods like Random Forest and Support Vector Machine in the current framework. In this work, we propose a unique use of transfer learning algorithms to thyroid nodule categorization. Neural network models that have already been trained on large datasets are modified for specific tasks that require less data through the use of transfer learning. Our approach involves using a state-of-the-art convolutional neural network (CNN) that has been pre-trained on a range of medical pictures to extract significant information from thyroid ultrasound scans. To optimize its performance for accurate classification, the model is trained on a particular dataset of thyroid nodule images. We examine the effectiveness of many transfer learning architectures, such as VGG16 and Xception CNN, and assess their overall accuracy, sensitivity, and specificity. The proposed methodology aims to provide physicians with a reliable thyroid problem diagnosis tool by increasing the categorization efficiency of thyroid nodules. The results pave the way for more precise thyroid image analysis, diagnosis by demonstrating how transfer learning can be utilized to maximize model performance even in the presence of sparsely labelled medical data.