Artificial intelligence is rapidly transforming modern engineering systems, enabling unprecedented levels of autonomy, adaptability, and real-time decision-making in industrial automation, robotics, and intelligent transportation systems. However, the increasing deployment of complex machine-learning models in safety-critical applications introduces significant challenges related to transparency, interpretability, reliability, and trustworthiness. This invited plenary paper presents a synthesis of the authors’ recent research results focused on trustworthy edge intelligence for engineering applications, with particular emphasis on explainable artificial intelligence (XAI), edge computing, and resource-constrained intelligent systems. The paper presents recent developments in explainable AI approaches for industrial automation and robotics, including hybrid deep-learning and ANFIS-based architectures for interpretable industrial decision support and machine-vision applications. Recent results in TinyML- and Industrial Internet of Things (IIoT)-based intelligent edge systems for low-cost industrial quality control are also discussed. In transport engineering, special attention is devoted to edge-AI implementations for intelligent transportation systems based on YOLO and Grad-CAM approaches deployed on embedded platforms such as NVIDIA Jetson devices and Raspberry Pi systems equipped with AI accelerators. Comparative analyses of performance, explainability, energy efficiency, and deployment suitability are presented. Finally, future directions toward human-centered, explainable, and trustworthy intelligent engineering systems are discussed in the broader context of engineering today and emerging Industry 5.0 paradigms.