The rapid growth of internet of things (IoT) applications, especially in wireless sensor networks (WSNs), has led to the generation of large amounts of real-time data from interconnected devices. This growth leads to challenges in securing data access and managing resources efficiently. To address these challenges, we propose a dynamic trust-based access control (DTAC) model for IoT and WSN applications. The DTAC model integrates behavioral trust evaluation and context-aware decision making to dynamically adapt access permissions to network conditions in real-time. the trust scores are calculated using fuzzy logic and machine learning techniques, which enable adaptive decision-making. To increase security, the model uses a hybrid encryption scheme that combines elliptic curve cryptography (ECC) with lightweight symmetric encryption, ensuring data confidentiality with minimal computational overhead. In addition, access control decisions are refined by contextual factors such as user roles, device locations, and data sensitivity. The model includes a collaborative re-evaluation mechanism that periodically updates trust scores and isolates malicious nodes without compromising network stability. The DTAC model is evaluated on key metrics such as security resilience, energy efficiency, and latency and demonstrates better performance than existing solutions. This model provides a scalable, energy-efficient, and secure framework for IoT and WSN applications that ensures reliable data access and privacy in diverse environments.