The emergence of Digital Twin (DT) technology holds immense promise for revolutionizing modern communication systems, particularly in the context of future Metaverse-based networks. DT involves digitally defining and modeling physical entities, facilitating feedback optimization through simulation, control, and prediction. While DT has seen success in manufacturing and complex system operation, its application in next-generation (nextG) networks is nascent, facing challenges such as building high-fidelity twins, adapting to dynamic network nature, and integrating with advanced machine learning (ML) models. Digital Twin Networks (DTNs) offer a compelling solution, providing benefits like accelerated network development, enhanced resource utilization, and streamlined management. In a DTN, virtual replicas of physical entities form a comprehensive network system, enabling real-time simulation, monitoring, and analysis. DTNs find application across network deployment, operation, and design phases, facilitating remote commissioning, diagnostics, and predictive analytics to optimize efficiency and performance. Despite the challenges, the fusion of ML techniques with DTNs offers tangible benefits across resource allocation, channel estimation, and user behavior prediction. This chapter aims to explore the synergy between ML and DT in addressing these challenges and leveraging opportunities, highlighting key insights for DTN-enabled network applications.