Poultry farming plays a vital role in meeting the rising global demand for animal protein. However, traditional techniques for monitoring broiler chickens have limitations. Manual broiler counting is time-consuming and prone to errors. This study explores using thermal imaging and advanced deep learning for automated broiler detection and counting. A dataset of 5000 thermal image frames of 2000 chickens was created for training, validation, and testing. Two deep learning models, YOLOv7 and YOLOv8, were compared. Thermal image features were extracted using these models to capture broiler-related thermal patterns. The study addresses the challenge of automating broiler detection and counting with high accuracy and efficiency. YOLOv8 outperformed YOLOv7, achieving significantly higher mean Average Precision (mAP) of 95% compared to 85%. Faster convergence within 20 epochs compared to 60 epochs for YOLOv7. In addition, YOLOv8 exhibited lower error rates of 2% vs. 5% for broiler counting tasks. This research demonstrates the effectiveness of YOLOv8 for real-time precision agriculture applica-tions using thermal imaging and deep learning for poultry monitoring. The findings pave the way for implementing automated broiler detection and counting systems in poultry farms, improving efficiency and data accuracy.