The maritime industry is increasingly adopting digital technologies to enhance the reliability, safety, and efficiency of vessel operations. Among these innovations, Digital Twin (DT) technologies have emerged as a key enabler of intelligent maintenance systems. This article presents a comprehensive review of the role of DTs in supporting prescriptive maintenance – a data-driven approach that not only predicts equipment failures but also recommends optimal maintenance actions. The review explores the conceptual foundations of DTs, their system architecture, and the algorithmic frameworks that enable decision-making in complex maritime environments. It also examines real-world applications and case studies, highlighting how DT-based prescriptive maintenance contributes to reduced downtime, improved asset utilization, and cost-effective operations. Key implementation challenges are discussed, including data integration, real-time modeling, and the need for standardization. The article concludes with a proposed research agenda aimed at advancing autonomous and resilient maintenance systems in maritime transport.