The algorithmic guardians: AI and computer vision for global faunal welfare, conservation, and future policy trajectories in the Indian subcontinent

Artificial Intelligence (AI) and Computer Vision (CV) are rapidly transforming animal welfare, conservation, and ecosystem management by enabling scalable, real-time analysis of large multimodal datasets. Traditional monitoring approaches are increasingly inadequate due to the exponential growth of visual and sensor data across farms, urban ecosystems, and wildlife habitats. This paper presents a structured review of AI/CV methodologies—including convolutional neural networks, You Only Look Once (YOLO)-based detection, and pose estimation—for quantitative faunal assessment. A systematic synthesis is provided across key domains such as precision livestock farming, urban animal welfare, wildlife conservation, and marine ecosystem monitoring. The study adopts a structured literature review methodology, outlining database selection, inclusion criteria, and comparative evaluation of state-of-the-art techniques. Key findings indicate that AI-driven systems significantly enhance early disease detection, behavioral analysis, and conservation efficiency, though challenges persist in terms of data scarcity, algorithmic bias, and deployment constraints in low-resource environments. A comparative analysis highlights trade-offs between accuracy, computational efficiency, and scalability across different AI architectures. The paper also identifies critical research gaps, including the lack of standardized datasets, limited cross-species generalization, and insufficient integration with policy frameworks. Finally, the study proposes a conceptual framework integrating AI, edge computing, and ethical governance for sustainable faunal management. The findings underscore the need for interdisciplinary collaboration and responsible AI deployment to ensure equitable and scalable benefits across diverse ecological and socio-economic contexts.