Post Operation Clean Out (POCO) is the process to remove hazardous materials and decommission nuclear facilities at the end of a nuclear plant’s lifetime. The introduction of Internet of Things (IoT) technologies in the environment, especially radio frequency identification (RFID), would improve efficiency and safety by intelligently monitoring POCO activities. In this paper, we present a passive material identification and crack sensing method developed for the integration of sensing and communication using commercial off-the-shelf (COTS) RFID tags, which is a long-term solution to material property monitoring under insulation for harsh environmental conditions. To validate the effectiveness of material identification and crack monitoring, machine learning techniques have been applied, and the feasibility of the study has been outlined. The result shows that the material identification can be achieved with traditional features and obtain improved accuracy with three-layer multi-layer neural networks (MLNN). In crack characterization, the tree algorithm based on traditional features achieves a reasonable accuracy, while three-layer MLNN is the best solution, which supports the efficiency of traditional feature extraction methods in specific applications.