Trust management plays a crucial role in wireless sensor networks to ensure secure and dependable communication between nodes. This study presents a new ARC-Net model that utilizes adaptive penalty coefficients to dynamically modify trust levels based on node behavior. The ARC-Net model combines direct, indirect, and energy trust assessments, along with advanced optimization and machine learning methods, to effectively handle network energy efficiency and trust. The model’s efficacy is emphasized by its capacity to preserve network integrity until the initial node failure occurs at 8100 rounds, showcasing the system’s resilience and its potential to improve network longevity.