<p>The rail bridge systems are an integral component of the transport system the temperature changes and train cyclic loads as well as the environment continuously exerts complex thermo-mechanical loading on them. It is not easy to assess the reliability due to the nonlinearity of the relationship between thermal stresses, material degradation and structural responses. In this research, a Fuzzy-Integrated Thermo-Mechanical Physics-Informed Neural Networks (FI-TMPINN) is proposed to give a reliable answer to the validity of the railway bridge system and given method to use the physical laws such as heat transfer equations and as well as structural mechanics within the training framework of the neural network, thus providing a physically consistent and data efficient model. Multi-source data like temperature distributions, strain responses and load characteristics are modeled to give predictions on the stress fields, deformation behavior and potential areas of failure. A thermo-mechanical analysis is coupled with an aim of having a superior representation of the interaction of thermal gradients and dynamic loads in diverse conditions of operations. The proposed FI-TMPINN provides the lowest MSE (0.0082), highest <i>R</i>² (0.978), highest prediction accuracy (96.8%), highest prediction reliability index (<i>β</i> = 3.42), lowest prediction failure probability (1.95%), and has the highest computational efficiency (96.8%) with only 9.2&#xa0;ms inference time required for real-time prediction. Furthermore, the fuzzy reasoning system embedded in the system appropriately represents the uncertainty caused by the non-uniform operating conditions and can classify the structural status and make maintenance decisions with high reliability. The results presented in the research demonstrate the potential of the proposed framework, FI-TMPINN, for accurate, physically consistent and efficient SHM and predictive maintenance of railway bridges, which is intelligent.</p>