This paper addresses the challenge of localizing unknown sensor nodes in wireless sensor networks (WSNs) for smart irrigation systems, aiming to minimize water consumption while maintaining crop quality. The proposed method uses estimated range measurements and the Levenberg–Marquardt (LM) optimization algorithm to accurately determine the positions of unknown nodes, even when they are far from anchors. Extensive simulations and experiments demonstrate the effectiveness and scalability of the proposed method, achieving 19% and 58% improvement in estimation accuracy compared to the distance vector-hop (DV-Hop) and semidefinite relaxation-LM (SDR-LM) algorithms, respectively. The method also exhibits robustness against measurement noise and scalability for large-scale networks. The integration of this localization method into smart irrigation systems is expected to reduce water consumption by approximately 28%. The research highlights the potential of accurate node localization in optimizing water usage and enhancing sustainable agricultural practices.This paper addresses the challenge of localizing unknown sensor nodes in wireless sensor networks (WSNs) for smart irrigation systems, aiming to minimize water consumption while maintaining crop quality. The proposed method uses estimated range measurements and the Levenberg–Marquardt (LM) optimization algorithm to accurately determine the positions of unknown nodes, even when they are far from anchors. Extensive simulations and experiments demonstrate the effectiveness and scalability of the proposed method, achieving 19% and 58% improvement in estimation accuracy compared to the distance vector-hop (DV-Hop) and semidefinite relaxation-LM (SDR-LM) algorithms, respectively. The method also exhibits robustness against measurement noise and scalability for large-scale networks. The integration of this localization method into smart irrigation systems is expected to reduce water consumption by approximately 28%. The research highlights the potential of accurate node localization in optimizing water usage and enhancing sustainable agricultural practices.