Figures Abstract Energy efficiency and balanced load distribution remain persistent challenges in the Routing Protocol for Low-Power and Lossy Networks (RPL), significantly affecting the lifetime and reliability of Internet of Things (IoT) deployments. This study introduces EDCC-RPL, a novel multi-metric objective function that integrates Expected Transmission Count (ETX), end-to-end delay, and child count through an adaptive additive model. Unlike conventional single or dual metric schemes such as OF0, MRHOF, and EA-EPL, EDCC-RPL simultaneously enhances energy efficiency, network stability, and scalability. Extensive simulations in Contiki Cooja with 20–50 nodes demonstrate up to 32% lower energy consumption, 18% higher Packet Delivery Ratio (PDR), and 50–60% reduction in parent switching (churn) in dense topologies. These improvements validate EDCC-RPL’s novelty in achieving joint optimization of reliability, delay, and load balancing. The proposed approach provides a practical and scalable solution for sustainable IoT networks in smart cities, industrial monitoring, and environmental sensing applications. The implementation code of the proposed EDCC-RPL algorithm is publicly available at https://github.com/drmasifhabib/edcc-rpl-github for reproducibility and reuse. Citation: Habib MA, Albarrak AM, Ahmad M, S. Ahmed AE, Raza N, Sajid Imran HM, et al. (2026) EDCC-RPL: A novel energy-efficient and load-balanced objective function for RPL Routing in IoT networks enhancing network lifetime and reliability. PLoS One 21(4): e0346827. https://doi.org/10.1371/journal.pone.0346827 Editor: Joanna Tindall, PLOS: Public Library of Science, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND Received: September 6, 2025; Accepted: March 24, 2026; Published: April 21, 2026 Copyright: © 2026 Habib et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: All relevant data are within the paper. The implementation code of the proposed EDCC-RPL algorithm is publicly available at https://github.com/drmasifhabib/edcc-rpl-github for reproducibility and