Wireless Sensor Networks play a vital role in the Internet of Things, smart cities, and industrial automation, yet there are open ended challenges in terms of efficient energy management and reliable data transmission. This paper presents a novel, two-phase routing framework comprising Dynamic Channel Selection and Energy-Efficient Routing Optimization to address these issues. In the first phase, Deep Q-Learning is utilized to identify stable communication channels, thereby enabling congestion-free data transfer across the network. The second phase implements Coral Reef Optimization to derive energy-efficient routing paths, significantly minimizing power consumption. Additionally, Adaptive Modulation and coding dynamically adjusts transmission parameters in real time to improve data throughput and reduce network delays. Existing solutions have been limited by network instability, poor scalability, and inefficient spectrum usage; In contrast, the integrated approach leverages Deep Q-Learning for intelligent channel allocation and Coral Reef Optimization for optimized route selection, while Adaptive Modulation and Coding fine-tunes the communication process to achieve optimal performance. Compared to existing models which shows high packet drop ratio and scalability constraints, our model achieves a 68% reduction in energy consumption, increases network lifetime by 82%, lowers error rate by 77%, enhances routing stability by 85%, and boosts overall throughput by 79%. These results highlight the proposed model’s potential as a highly adaptive, low-latency, and scalable solution for next-generation wireless sensor network applications.