<p>Digital twins (DTs) are live virtual replicas of physical objects, systems, or biological processes that receive continuous real-time data from their physical counterparts. By fusing sensor inputs, sophisticated algorithms, and machine learning, these models can simulate, monitor, and forecast the behaviour of the actual entity. In healthcare, patient-specific digital twins offer surgeons powerful tools for training and performing procedures with heightened precision and situational awareness. They function as the most personalised simulator available, faithfully replicating an individual’s unique anatomy (including all variations), physiology (with its specific quirks), and pathology—complete with exact dimensions, location, extent, vascular supply, and associated risks. Digital twins exist in four primary categories—static, functional, shadow, and intelligent. In addition to supporting preoperative planning, team rehearsals, trainee education, and patient counselling, they improve surgical safety, accuracy, and outcomes. Whether the task is complete tumour excision, orbital repositioning during facial bipartition, or achieving optimal aesthetic and functional results in bimaxillary orthognathic surgery, DTs are poised to become indispensable. They promise greater surgical precision, reduced complications, and truly individualised interventions—benefits that are especially valuable in aesthetic surgery and complex reconstructions following trauma or ablative cancer procedures. Nonetheless, robust protection of patient data privacy is essential (particularly on cloud platforms), and the high implementation cost still restricts widespread adoption. Level of Evidence: not gradable</p>