From Japanese cypress to American yellow pine, wood has long been a staple in the construction industry. Although steel and concrete dominate large-scale construction, wood has regained popularity in public and multi-story buildings in recent years due to its environmental advantages. Researchers have developed a new method that combines infrared spectroscopy with machine learning to detect early signs of wood coating deterioration. This approach not only enhances the accuracy of coating inspections but also provides crucial early warnings for timely maintenance, thereby extending the lifespan of wood structures and reducing resource waste. Traditional methods of inspecting wood coatings often rely on visual assessments, which can be ineffective in identifying subtle early changes. In contrast, infrared spectroscopy can detect early signs of deterioration by analyzing chemical changes in the coating. However, this technique generates large volumes of data that require specialized knowledge to interpret. To address this challenge, researchers introduced machine learning algorithms. By training on extensive infrared spectroscopy data, these algorithms can automatically recognize and alert users to the early signs of coating degradation. The successful implementation of this technology has several significant benefits. It improves the durability of wood buildings, reduces maintenance costs associated with coating issues, and paves the way for broader applications of wood in construction. As the demand for sustainable building materials continues to rise, this advance is expected to have a lasting impact within and beyond the industry.