Recent research in routing protocols for Underwater Sensor Networks focuses on developing energy-efficient, reliable, and adaptive mechanisms to overcome the challenges of high propagation delay, limited bandwidth, and node mobility in acoustic environments. Current studies propose hybrid and intelligent routing schemes that integrate depth-based, location-aware, and opportunistic methods to enhance data delivery and reduce latency. Machine learning and reinforcement learning techniques are increasingly employed to predict optimal routing paths and adapt to dynamic underwater conditions. Additionally, void avoidance, trust-aware, and cooperative routing strategies are gaining attention for improving network resilience and packet delivery ratio in harsh underwater conditions, ensuring efficient communication for applications such as marine monitoring, resource exploration, and environmental sensing.