Purpose of review <p>This review explores the potential of big data analytics and machine learning (ML) in transforming geriatric anesthesia to address the challenges posed by the growing elderly population.</p> Recent findings <p>ML algorithms have shown promise in enhancing preoperative risk stratification, optimizing intraoperative anesthesia management, and predicting postoperative complications in elderly patients. ML models can accurately forecast adverse outcomes such as delirium, respiratory complications, and mortality, that could enable targeted interventions and personalized care.</p> Summary <p>The integration of ML and big data analytics in geriatric anesthesia holds immense potential to revolutionize perioperative care for older adults. By leveraging data-driven insights, anesthesiologists can deliver precise, patient-centered care that improves outcomes and safety. However, the responsible and ethical deployment of these technologies requires addressing challenges related to data privacy, algorithmic bias, and model interpretability. Close collaboration between anesthesiologists, data scientists, and other stakeholders is crucial for the successful adoption of ML in geriatric anesthesia practice.</p>