<p>Thyroid Nodule (TND) refers to an abnormal structure composed of cells within the thyroid gland. These nodules are mostly benign in nature, but a small portion of nodule might be cancerous. Therefore, early identification and classification plays a significant role. Ultrasonography (USG) is a key tool for the thyroid nodule identification. USG uses a high- frequency sound waves to obtain picture of the nodules. Deep Learning (DL) provides a cutting-edge result to the various Machine Learning (ML) techniques and computer vision tasks. In this article, Generative Adversarial Network-Alex based synthetic ultrasound images augmentation model is proposed for TND classification. The Generative Adversarial Network-Alex model works in four phases: (1) data acquisition, (2) pre-processing, (3) data augmentation using Generative Adversarial Network (GAN), (4) classification using AlexNet. The model is evaluated on public and collected datasets having 295 and 428 thyroid ultrasonography (USG) images. GAN technique for data augmentation and Grid Search Optimization (GSO) employed to identify the optimal set of hyperparameters (learning rate and optimizer) that maximize the performance of the AlexNet model. The proposed Generative Adversarial Network-Alex model has achieved an accuracy of 96.85%, specificity of 94.44% and sensitivity of 97.80% on dataset-1 and an accuracy of 98.18%, specificity of 95.65% and sensitivity of 98.85% on dataset-2. Generative Adversarial Network-Alex model experimental results indicate that the proposed model consistently outperforms state-of-the-art approaches documented in prior studies.</p>