The pharmaceutical industry is undergoing a significant transformation driven by digitalization and the adoption of Industry 4.0 principles. Among the most promising innovations is digital twin technology, which creates dynamic virtual replicas of physical manufacturing processes that enable real-time simulation, monitoring, prediction, and optimization. This review article examines the convergence of digital twin technology with established process validation frameworks in pharmaceutical manufacturing, with particular emphasis on the three-stage lifecycle approach outlined in the United States Food and Drug Administration (FDA) Process Validation Guidance and the International Council for Harmonisation (ICH) quality guidelines Q8 (R2), Q9 (R1), Q10, and Q13. The paper explores how digital twins can enhance each stage of process validation, from process design (Stage 1) through process qualification (Stage 2) to continued process verification (Stage 3), by providing mechanistic and data-driven models that improve process understanding, reduce development timelines, and support real-time decision-making. Key enabling technologies, including Process Analytical Technology (PAT), Internet of Things (IoT) sensor networks, machine learning algorithms, and cloud computing platforms, are discussed in the context of their integration with digital twin architectures. Case studies from both small molecule and biologic manufacturing are presented to illustrate practical applications. The article further addresses regulatory considerations, data integrity requirements, model validation challenges, and the ethical implications of adopting AI-augmented digital twins in GMP-regulated environments. Finally, future research directions, including the integration of quantum computing, multi-omics data, and federated learning approaches, are proposed. This review aims to provide pharmaceutical scientists, engineers, and regulatory professionals with a comprehensive roadmap for leveraging digital twin technology to achieve robust, compliant, and efficient manufacturing processes.