The application of digital twin technology in healthcare has emerged as a significant advancement, offering new possibilities for personalized medicine, surgical planning, and healthcare system optimization. By creating real-time, data-driven virtual models of patients, organs, and hospital environments, digital twins enable precise simulations, predictive analytics, and personalized treatment strategies. This review and meta-analysis evaluate the role of digital twins in improving clinical decision-making, patient outcomes, and operational efficiency while also addressing the technical, ethical, and regulatory challenges that hinder their widespread adoption. The analysis synthesizes data from studies examining digital twins in disease modeling, surgical rehearsals, and hospital resource management, highlighting their ability to enhance patient-specific interventions and optimize healthcare workflows. The results suggest that digital twins significantly improve diagnostic accuracy, treatment efficacy, and hospital efficiency, demonstrating their potential as transformative tools in modern medicine. However, challenges such as data integration complexities, patient privacy concerns, computational demands, and regulatory uncertainties must be addressed to ensure reliable implementation. This study underscores the importance of further research, cross-disciplinary collaboration, and the establishment of standardized regulatory frameworks to facilitate the responsible integration of digital twin technology in healthcare. With continued advancements and appropriate safeguards, digital twins can contribute to more precise, efficient, and patient-centered healthcare delivery, paving the way for a new era of medical innovation.