Research in routing protocols for Delay Tolerant Networks (DTNs) focuses on developing intelligent, adaptive, and resource-efficient mechanisms to ensure reliable data delivery despite frequent disconnections and unpredictable mobility patterns. Recent works explore context-aware and opportunistic routing schemes that leverage encounter history, node mobility, and social relationships to improve message forwarding efficiency. Machine learning and deep reinforcement learning–based approaches are increasingly applied to predict contact probabilities and optimize routing decisions dynamically. Energy-efficient and buffer-aware routing protocols are also designed to minimize resource consumption and transmission delay. Additionally, blockchain-integrated and trust-based DTN routing models are emerging to enhance data integrity and prevent malicious behavior. These advancements significantly improve delivery ratio, reduce latency, and enhance overall network performance in highly dynamic and delay-prone DTN environments.