As the core industry of energy supply, the efficiency of storage and logistics management of thermal power plant has a direct impact on the safety, economy and sustainability of power generation. At present, the traditional thermal power plant warehousing and logistics system is faced with decentralized management, information lag, high dependence on manual and insufficient cost control, which cannot meet the needs of modern management. The in-depth application of Internet of Things (IoT) technology provides technical support for the digital upgrade of warehousing and logistics system, but there are still challenges in data integration depth, prediction model accuracy and adaptability to complex environment. The intelligent warehouse and logistics management system developed in this study for thermal power plants integrates IoT, AI, and automation technologies to create a smart management platform that covers full lifecycle tracking of material information, automated warehouse scheduling, intelligent logistics path optimization, and multidimensional data analysis. By deploying RFID tags, smart sensor terminals, and AGV logistics equipment, combined with recursive neural networks and reinforcement learning algorithms, the system achieves real-time material status monitoring, precise inventory demand forecasting, and dynamic optimization of transportation routes.