Precision Livestock Farming (PLF) technologies may boost the well-being of livestock by providing real-time welfare evaluations and encouraging timely therapies, their ultimate objective remains undefined. The livestock digital revolution delivers constant surveillance of the welfare of livestock at both the group and individual levels. To evaluate changes in behavioral patterns or physiological indicators, multiple sensors and data analytics are employed. PLF systems empower farmers with excellent real-time monitoring and management capacities, facilitating immediate intervention in scenarios associated with production concerns. Creating effective real-time algorithms for such platforms requires adherence to fundamental principles. Precision Nutrition (PN), a subsection of the PLF technique, encompasses the timely distribution of nourishment to livestock, necessitating automated data collection, processing, and supervision measures. Deploying such mechanisms appears to be complicated. This study suggests a hybrid offline–online training approach for long-term behavioral monitoring systems in precision livestock farming. The method addresses the concern of concept drift by guaranteeing consistent training data, which improves both animal welfare and production. This article examines recent advancements in PLF to assess the welfare of dairy cattle, encompassing physical condition, mastitis, and lameness. It also examines at how PLF data may be integrated into an expanded welfare assessment framework. Real-time sensing technologies are employed for determining nutritional requirements, therefore supporting the sustainability pillars of financial, ecological, and ethical conduct.