Wireless Sensor Networks (WSNs) offer a broad spectrum of applications but are unstable and vulnerable to malicious attacks due to the limited energy and memory of sensors. Trust management is essential for identifying suspicious behavior, completing traditional security techniques in WSNs. This paper addresses the critical challenge of detecting malicious nodes efficiently within complex environments. In this paper, a trust management system utilizing the Markov chain model is proposed to calculate short-term trust and assess node states, aiming to mitigate environmental complexities. The Markov chain model provides a robust framework for evaluating node behavior in real-time, ensuring rapid identification of anomalies. Additionally, a novel model for calculating long-term trust is introduced, incorporating practical factors to reduce the impact of fluctuating environmental parameters. This dual approach not only enhances the accuracy of trust assessments but also improves the resilience of the network against various types of attacks. Simulation results demonstrate that the proposed algorithm effectively identifies malicious nodes and achieves a higher packet delivery ratio (PDR) compared to existing methods, thereby enhancing overall network security. The findings indicate that the trust management system can significantly improve the reliability and security of WSNs, making them more robust against potential threats.