Research in mobility models for Delay Tolerant Networks (DTNs) focuses on accurately simulating node movement patterns to improve message delivery performance and protocol evaluation in intermittently connected environments. Recent studies propose realistic and context-aware mobility models that reflect human, vehicular, and aerial movement behaviors observed in real-world DTN scenarios. Social-based and group mobility models are increasingly adopted to capture community structures and encounter dynamics, improving the predictability of contact opportunities. Machine learning and reinforcement learning techniques are also being used to forecast mobility patterns and optimize routing decisions based on predicted encounters. Hybrid and trace-driven mobility models further enhance realism by integrating spatial, temporal, and social parameters. These advancements enable more accurate analysis of DTN performance, leading to the design of efficient, adaptive, and scalable communication protocols for challenging and delay-prone networks.