In order to investigate the early warning method for bridge assessment under random train loading with joint distributed macro-strain monitoring and machine learning, and to realize the distributed rapid assessment of railroad bridges, this study establish a three-dimensional refined finite element model of vehicle-rail-bridge coupled vibration. It is also apply load statistical analysis methods to construct a stochastic traffic flow model that is suitable for actual train operation, and based on the principle of distributed monitoring, propose a distributed macro-strain influence line area as the indicator design warning interval evaluation warning method for bridge warning; Furthermore, through simulation analysis of various stiffness degradation conditions, a distributed macro-strain monitoring data sample library under random train loads was constructed to compare and study the accuracy of 4 machine learning methods in quantifying and locating bridge damage. The results show that all 4 types of machine learning are able to localize and quantify the localized damage of bridge structures with an average recognition accuracy of 90.0%, with the KNN model and the SVM model performing the best in the test of quantifying bridge damage, both with 95.0% recognition accuracy, and the SVM model performing the best in the test of locating the damage of the bridge structure, with a recognition accuracy of 98.3%. The joint distributed macro-strain monitoring and machine learning approach for bridge assessment has feasibility, SVM model performs best in the test of bridge structure damage localization, KNN model and SVM model perform best in the test of bridge damage quantification, and in the comprehensive analysis, SVM performs best in bridge damage localization and damage quantification analysis.