The importance of inspection and maintenance for operational safety, asset lifetime, and minimal downtime in areas as diverse as manufacturing, energy or transportation is difficult to overestimate. Conventional inspection techniques are slow, error-prone, and dependent on human expertise. To meet rising requirements for accuracy, efficiency and real-time control, the integration of Augmented Reality (AR) and Artificial Intelligence (AI) comes up as a disruptive solution. In this paper presenting an AI-backed intelligent object detection system, deployed in AR-space, to facilitate real-time inspection and maintenance activities. The presented system combines computer vision methods, object recognition using deep learning and AR visualization, so that the technicians can recognize the components, find anomalies and receive context-aware instructions during inspection. Based on the existing advanced models, such as YOLOv8, Vision Transformers, etc., the framework is then designed for training activities detecting already known defects such as the part of the equipment, rust, cracks, leakage points and other operations defects. When problems are identified, they are then superimposed in real time via AR headsets/ or mobile devices so users can view the detailed diagnostics, repair instructions, along with safety alerts – all without interruption of workflow. The architecture has four layers such as Data acquisition (smart cameras, IoT sensors), AI based processing (edge, cloud), AR based user interaction, and Feedback Mechanism (real time decision support). One major bright spot is that the system can self-learn to have adaptation to the ambient and lighting changes and thus the ability to retain the high accuracy of detection under different industrial scenes, achieved by transfer learning and context recognition. Furthermore, with real-time synchronization, detected anomalies are logged and transmitted to cloud dashboards for monitoring, auditing, and predictive maintenance analytics. We evaluate our method on industrial datasets such as turbine inspection imagery and pipeline monitoring video feeds and achieve accuracy above 92% with under 250 ms average latency for overlay rendering. AI and AR combined together has led to huge time savings, increased safety by removing the need for on-site technicians and decision-making benefits with guided diagnostics. Furthermore, the system promotes teamwork by providing live visual flow remote experts for reference, which effectively simplifies the maintenance process. This work shows that object detection in AR-enabled environments, based on AI, is a strong driver for intelligent industrial maintenance. “Combining situational awareness with machine intelligence, the method presented is scalable, efficient and holistic for industrial inspection. It is an essential step towards Industry 5.0, where human knowledge is enhanced by intelligent systems in real-time to make better decisions.