Pollinators provide critical ecosystem services valued at billions of dollars annually, yet populations face unprecedented decline due to habitat loss, pesticide exposure, pathogens, and climate change. Traditional monitoring approaches rely on manual surveys lacking temporal resolution and failing to capture dynamic physiological and behavioral responses to environmental stressors. Bio-digital integration—combining biosensors, wearable tracking devices, Internet of Things platforms, and artificial intelligence—offers transformative capabilities for real-time assessment of pollinator health at individual and colony levels. This review examines cyber-physical systems designed for pollinator monitoring, emphasizing miniaturized biosensors for physiological parameter measurement, radio-frequency identification and radar-based tracking technologies, edge computing architectures for distributed data processing, and machine learning algorithms for pattern recognition and anomaly detection. Applications span precision agriculture, where real-time pollinator activity data inform crop management decisions, and conservation biology, where early warning systems detect disease outbreaks and toxicological exposures. Despite technological advances, challenges persist in device miniaturization, energy autonomy, data interoperability, and ethical deployment. Future systems will require standardized protocols, multi-stakeholder engagement, and scalable architectures balancing ecological sensitivity with agricultural productivity.