The proliferation of the internet of things (IoT) has led to the widespread adoption of wireless sensor devices that connect physical objects and people to the Internet. Despite their potential, IoT networks are constrained by limited power and memory resources, necessitating the development of efficient routing protocols to ensure long-term operational effectiveness. In response to this challenge, this study introduces a novel deep reinforcement learning energy-efficient routing (DRLEER) approach that optimizes routing decisions by enabling IoT devices to learn from experience and adapt to dynamic network conditions, including energy levels and mobility. The deep reinforcement learning agent is trained on a comprehensive dataset encompassing network topology, traffic patterns, and energy consumption, facilitating the selection of optimal routing decisions. The genetic optimization algorithm is incorporate with effective cluster head identification by considering the factors such as device energy levels and node proximity, DRLEER minimizes energy consumption and extends the lifespan of IoT devices. The proposed method is benchmarked against various existing protocols in terms of scalability, network durability, and energy efficiency. The findings reveal that DRLEER consistently outperforms its counterparts across all metrics, yielding substantial enhancements in network efficiency, energy consumption, and overall performance. © 2023 Elsevier B.V., All rights reserved.