The expansion of Internet of Things (IoT) devices depends on a consistent mechanism for managing resources in real-time and safe data transmission. Conventional cloud-based IoT systems are not suitable for immediate response applications due of security concerns, high latency, and ineffective resource use. For addressing these problems, Adaptive Trust-Driven Edge Intelligence for IoT (ATEI-IoT) has been presented in this paper as an emerging edge-enabled framework improving computing efficiency, dynamic resource allocation, and secure communication. ATEI-IoT offers a Trust-Driven Adaptive Resource Management (TARM) approach that dynamically allocates edge computing resources in a distributed manner by real-time evaluation of device dependability and workload needs. This reduces processing latencies to almost zero by giving dependable devices top priority and distributing computing loads as uniformly as feasible over the network. Furthermore, allowed by low-processing-power IoT devices is data sharing with low latency and encryption via a Lightweight Secure Transmission Protocol (LSTP). Using secure transmission and adaptive resource scheduling, ATEI-IoT is a resolution for next-generation IoT networks. Comprehensive simulations are conducted by research to evaluate ATEI-IoT with latency, throughput, energy efficiency, and security performance as its goals. Comparatively to conventional cloud-based IoT systems, processing delay is dropped by 28.7%; resource use is improved by 94.1%; and security resilience is raised by 98.5%. Combining trust-driven intelligence with adaptive resource optimization guarantees real-time processing and consistent data transfer, providing a scalable, safe, and energy-efficient solution for future IoT ecosystems.