Digital twin (DT) technology has emerged as a critical enabler of Industry 4.0, facilitating real-time synchronization between physical systems and their virtual counterparts. Despite rapid advancements, challenges related to scalability, interoperability, and data governance continue to limit widespread adoption. This study employs a systematic literature review approach, guided by PRISMA principles, to analyze recent research on digital twin architectures, enabling technologies, applications, and implementation challenges. Peer-reviewed articles published 2020 - 2025 were retrieved from major academic databases and synthesized using a thematic analysis framework. The review identifies key technological enablers, including IoT, artificial intelligence, cloud-edge computing, and big data analytics, which collectively enhance the capabilities of digital twins. Findings indicate that digital twins significantly improve operational efficiency, predictive maintenance, and decision-making across domains such as manufacturing, healthcare, and smart cities. However, limitations persist in terms of standardization, real-time scalability, data quality, and cybersecurity. Emerging solutions such as explainable AI, Blockchain, and federated learning show promise in addressing these challenges. While digital twin technology demonstrates substantial transformative potential, its full realization depends on the development of standardized, secure, and scalable architectures. Future research should prioritize interoperability frameworks, large-scale empirical validation, and human-centric system integration to support real-world deployment.