Fine-needle aspiration biopsy (FNAB) is a fundamental diagnostic procedure for thyroid nodules, in which accurate judgment of puncture positioning, specifically determining whether the needle tip is correctly located within the nodule, is essential for reliable pathological assessment. However, evaluating puncture positioning during FNAB remains challenging due to the complex and subtle visual cues in ultrasound images. To address this, we propose UL-TPNet, an ultra-lightweight deep learning-based network for Thyroid Puncture Positioning Detection guided by Nodule Location. With only 0.3M parameters, UL-TPNet achieves precise identification of the needle tip’s position relative to the nodule, providing real-time and accurate assistance to clinicians during FNAB procedures. To validate the effectiveness of our approach, we constructed the Thyroid Nodule Ultrasound (TNUS) dataset, which includes fine-grained annotations of puncture positioning. Experimental results demonstrate that UL-TPNet achieves an accuracy of 99.31% on the TNUS dataset, while maintaining exceptional computational efficiency. Overall, UL-TPNet offers a reliable, efficient, and lightweight solution for puncture positioning detection in thyroid nodule FNAB, with strong potential to enhance both procedural safety and diagnostic accuracy in clinical practice.