With the continuous advancement of science and technology and the increasing demand for quality of life, there is a growing need for enhanced safety and reliability in the power and communication sectors. Converter valve systems, as critical components in power transmission, are prone to performance degradation and failure due to equipment aging, which can lead to severe accidents such as fires, posing significant threats to both the social economy and public safety. To address these challenges, this study proposes an innovative real-time monitoring model for converter valve systems based on the integration of infrared and visible image fusion recognition technology and intelligent algorithms. The proposed approach leverages advanced infrared information and communication technologies, which have been widely adopted in industrial and engineering domains due to their strong adaptability to complex and high-cost system requirements. Through experimental validation, the developed model demonstrates notable improvements over traditional monitoring methods, achieving an 11% increase in fault prediction accuracy and significantly reducing monitoring time. The results indicate that the proposed model effectively enhances the safety, intelligence, and operational efficiency of converter valve systems, providing a promising direction for the future development of intelligent monitoring in power engineering applications.