The depth-based routing methods use the greedy algorithm to forward data packets, where sensor nodes passively receive data packets. Although some methods have been used to limit the redundancy, the area around the forwarding nodes is still subject to flooding, resulting in energy waste. Additionally, due to the acoustic communication between the underwater sensor nodes, the transmission rate is limited, and excessive packet transmissions in the network easily lead to the failure of important packet forwarding. With the development of artificial intelligence (AI), increasingly complex AI technologies are being used to design routing protocols [22]. Intelligent routing protocols have been proposed to address the challenges faced by traditional routing protocols in the UASN. These protocols can dynamically adapt to changes in the network environment, select the optimal path based on the real-time monitoring of network status and node changes, and optimize the energy consumption to reduce the routing void, minimize the packet delivery delay, and prolong the network life. In addition, intelligent routing protocols enhance the network security by selecting a more secure path. In [23], a fusion algorithm of ant colony optimization algorithm (ACOA) and artificial fish swarm algorithm (AFSA) was proposed for the routing protocol in the UASN. An adaptive mechanism is used to combine the advantages of ACOA and AFSA. The method first uses the AFSA to calculate a set of globally optimal paths. To address the problem of insufficient precision in the optimal path calculation of the AFSA, a parallel ACOA is then employed to select the optimal path. However, the method is not suitable for resource-limited underwater nodes due to the high computational complexity. In [24], a Q-learning-based localization-free anypath routing (QLFR) protocol was proposed, where the calculation of the Q-value involves the simultaneous consideration of both the residual energy and the depth of the sensor nodes. Furthermore, a new holding time mechanism was developed for data packet forwarding, taking into consideration the priority of forwarding candidate nodes. Nevertheless, the intricate mechanism and substantial computational demand involved pose challenges for implementation in the underwater environment. In [25], a reinforcement learning-based opportunistic routing (RLOR) protocol was proposed by combining the advantages of opportunistic routing algorithms and reinforcement learning algorithms. In addition, the RLOR protocol incorporates a recovery mechanism that effectively enables data packets to bypass void areas and seamlessly continue their forwarding process, resulting in an improved packet delivery ratio (PDR), particularly in sparse networks. However, the RLOR protocol uses a specific value combination which cannot be dynamically adjusted to accommodate changes in the environment. In [26], the deep Q-network (DQN)-based energy and latency-aware routing (DQELR) protocol was proposed. The DQELR protocol uses DQN to train agents since the Q-learning-based methods are not suitable for environments with a large state space. Each data packet is defined as an agent, and the depth and residual energy are considered when designing the reward function. The DQELR protocol can extend the network lifetime, as well as satisfy the energy consumption and latency constraints. However, the additional cost resulting from the Q-learning-related information exchange is not addressed. The routing methods mentioned above offer partial improvements in data transmission efficiency and energy consumption reduction. However, the challenges related to the high packet loss rate and prolonged end-to-end delay have not been addressed effectively. Furthermore, these methods lack the ability to adjust in real time and to recover the forwarding process when the data packets become trapped in routing voids. To address these issues, an adaptive support vector machine (SVM)-based routing (ASVMR) protocol is proposed for the UASN.