<p>Improving smart irrigation systems is essential for enhancing irrigation efficiency. However, the current systems often lack comprehensive data integration, a continuous virtual mirror, clear procedures for heterogeneous data, reactivity, and effective virtual testing. These limitations impede the efficient utilization of irrigation water. This review examines the potential of integrating Digital Twins with smart irrigation systems, aiming to provide a more comprehensive, adaptable, and predictive framework for sustainable irrigation management. It also intends to highlight critical research gaps and challenges in implementing Digital Twin-based smart irrigation. A systematic, comprehensive literature review was conducted on Digital Twin applications in smart irrigation, emphasizing their conceptual basis, five key components, and maturity levels. The research examined current smart irrigation systems, their limitations, and the potential of Digital Twins to advance irrigation systems, drawing on previous academic studies. This review highlights that, despite some limitations, such as data errors and model complexity, Digital Twins can improve smart irrigation and address deficiencies via real-time monitoring, enhanced data integration, adaptability, and predictive capabilities. This facilitates policy assistance for irrigation water management. This study also underscores the transformative potential of Digital Twins in creating efficient and sustainable irrigation systems. It advocates for establishing transparent standards, incorporating climate change strategies, integrating DTs into blockchain for water allocation, AI-enhanced twins for irrigation control, and participatory approaches that foster user trust and inclusivity. The insights enhance Digital Twin research and its correlation with Sustainable Development Goals connected to irrigated agriculture.</p>