Postoperative delirium (POD) is one of the most common and severe complications among older adults, and is closely associated with poor outcomes, increased healthcare costs, and long-term cognitive decline. With the rapid growth of the global aging population and the continuous emergence of new geriatric surgical procedures, the prevention and management of POD have become a major challenge. Traditional risk assessment tools have inherent limitations in clinical practice and are insufficient to meet the growing demands of precision medicine. In recent years, digital technologies have shown tremendous potential in perioperative management; innovations such as machine learning-based predictive models, wearable sensors, electroencephalogram (EEG) monitoring, and large language models are increasingly being adopted for POD risk identification. This review comprehensively summarizes research advances in digital technology for POD risk identification and their role in clinical decision support for older adults, with the aim of providing evidence-based insights and implementation guidance for clinical practice.