The increasing demand for efficiency, biosecurity, and reduced labor dependency in poultry farming has accelerated the adoption of automation and intelligent technologies. These systems are essential for improving animal health management and production efficiency. Among these technologies, deep learning (DL) has emerged as an effective solution, outperforming conventional image processing and machine learning approaches for poultry health monitoring and identification. This study is among the first to integrate a systematic review with a bibliometric mapping of DL applications in precision poultry farming. This study systematically reviews DLbased methods to identify major application areas and the models, datasets, and evaluation practices commonly used. An initial search retrieved 1,028 studies from five academic databases: IEEE Xplore, ScienceDirect, Wiley, SpringerLink, and Google Scholar. After applying predefined inclusion and exclusion criteria, 55 studies were selected for detailed analysis. These studies were used to answer the defined review questions. Two main application domains were identified: health-related and identification tasks. Poultry health monitoring was the dominant focus, accounting for 65% of the studies. The remaining studies focused on identification tasks. Among the eighteen reported DL models, Convolutional Neural Networks (CNNs) were the most widely used. Other commonly used models included YOLO variants, Long Short-Term Memory networks, and Mask-RCNN. Nineteen networks were identified, with ResNet-50, ResNet-18, ResNet-34, and ResNet-101 used most frequently. Twenty evaluation metrics were identified, although Matthew's Correlation Coefficient, One-off Accuracy, and Area Under the Curve appeared in fewer than five studies each. Bibliometric mapping further revealed major research themes, keyword relationships, and collaboration patterns, providing insight into the intellectual structure and development of this field. The findings highlight the growing importance of highquality datasets, lightweight architectures, and multimodal DL systems for future research and practical deployment. This study offers a structured reference for researchers and practitioners seeking to advance DL-based solutions in intelligent poultry health and identification systems.