Accurate clock synchronization is an important means to ensure network efficiency in many aspects such as military, environmental monitoring, and intelligent transportation. Therefore, this article intends to use artificial intelligence methods, combining deep learning and reinforcement learning, to adaptively optimize the transmission and reception process of synchronous signals, so that they can match the network environment. It uses deep neural networks to predict network latency and correction errors, and adopts reinforcement learning algorithms to adjust synchronization frequency and parameters online to improve network synchronization accuracy and resource utilization. In an ideal state, with a packet loss rate of 0% and optimal synchronization accuracy of 0.00 ms, this reflects perfect time synchronization. The research results of this article indicate that the wireless sensor network clock synchronization algorithm used in this article can effectively improve the clock synchronization accuracy of multi-channel communication systems, especially when the network topology is frequently changed or external interference is relatively large, it has better robustness and adaptive ability. The research results of this article can play a positive role in improving the overall performance of wireless sensor network (WSN) and provide new research ideas and methods for clock synchronization problems in other networks.