Precise categorization of sheep parasite eggs is essential for improved veterinary diagnostics, automated monitoring, early disease detection, and efficient farm management in modern agriculture. However, most existing deep learning models struggle to achieve both rich feature extraction and fast inference, often excelling at one while sacrificing the other. To address this limitation, we propose a DenseNet–YOLOv8 hybrid model that combines the dense connectivity and strong feature propagation of DenseNet with the anchor-free detection and high-speed inference of YOLOv8. The framework was trained and evaluated on a sheep parasite egg dataset under challenging conditions such as occlusion, illumination variation, and background clutter. Experimental results show that the hybrid model outperforms the individual DenseNet and YOLOv8 baselines across all tested metrics, achieving 96.8% accuracy, 95.7% precision, 96.3% recall, and a mAP@0.5 of 97.1%, while sustaining an inference speed of 82 FPS. Unlike previous approaches that were limited by slower inference or weaker feature representation, our model provides a balanced solution that is accurate and efficient, offering strong potential for integration into laboratory diagnostics and intelligent livestock health management systems.