The intersection of digital technology and animal welfare science is creating a new paradigm: precision welfare monitoring. This systematic review explores the latest digital tools used to assess, predict, and improve animal welfare in livestock systems. A comprehensive literature search across Scopus, Web of Science, and ScienceDirect (2015–2025) identified 142 peer-reviewed studies that met the inclusion criteria.

Findings show a clear shift from traditional, human-based welfare assessments toward automated, sensor-driven systems that allow continuous, non-invasive observation. The leading technologies include computer vision (for behavior and posture analysis), wearable biosensors (for physiological monitoring), and acoustic sensors (for stress and health detection). Increasingly, AI and machine learning are being used to interpret complex data streams, enabling early detection of welfare issues such as lameness, heat stress, or aggression.

However, several critical gaps remain—particularly around data integration, algorithm transparency, and ethical governance. While precision monitoring holds great promise for improving welfare and sustainability, it also raises important ethical questions about animal privacy, data ownership, and the potential detachment of human caretakers.

Future research should focus on ethical AI frameworks, cross-species validation, and education for farmers and caretakers to ensure that technology enhances welfare rather than merely improving efficiency. Overall, digital and precision welfare monitoring represent a transformative step toward data-driven, ethically responsible, and sustainable animal production systems.