The Wireless Sensor Network (WSN) is a network of numerous devices that are interconnected via the internet and significantly impact the network. However, despite their significant applications WSNs face challenges related to network security energy levels and information transmission delays. To address these challenges, a method utilizing Multi-Objective Deep Reinforcement Learning (DRL) has been proposed. The proposed method aims to maximize energy utilization in the network by efficiently managing covered and uncovered cluster network routing. The performance of energy transmission is enhanced through the use of the Markov Decision Process model based on multi-objective DRL combined with training the network using Deep Q Network (DQN) to reduce network energy consumption. Training the network with multiple objectives may pose challenges requiring more samples and leading to higher sample complexity, which can be a limiting factor in real-world applications. Despite this, the proposed multi-objective DRL method demonstrates high performance compared to existing methods such as Particle Swarm Optimization (PSO) and Convolutional Neural Network (CNN). Specifically, multi-Objective DRL method yields superior results, achieving an energy consumption of 42 J, Packet Delivery Ratio (PDR) of 90%, and an End-To-End Delay (ETED) of 45 S. These outcomes outperform existing methods in the context of WSNs.