Digital Twins (DTs) represent a groundbreaking development tool in the pharmaceutical and biopharmaceutical industries, providing virtual representations of physical entities, processes, or systems. This review investigates the transformative roles of DTs by examining their applications throughout the entire drug development lifecycle, from discovery to continuous manufacturing. By facilitating real-time monitoring and predictive analytics, DTs enhance operational efficiency, reduce costs, and improve product quality. Integration with advanced technologies, such as artificial intelligence and machine learning, further amplifies their capabilities, enabling sophisticated data analysis for preventive maintenance and manufacturing optimization. Despite these advantages, the implementation of DTs faces significant challenges, including data integration, model accuracy, and regulatory complexity. This review discusses these barriers while highlighting opportunities for innovation and automation through emerging technologies, including blockchain, nanotechnology, and dark factory. It also explores the potential of DTs to support personalized medicine through individualized treatments based on patient-specific data. Overall, this review highlights the current state, key challenges, and future perspectives of DT applications in pharmaceutical systems, emphasizing their potential to improve efficiency, quality, and patient outcomes.