<p>In order to investigate the multi-parameter precursor information during the damage and failure process of coal mass, this study utilized an independently developed integrated acoustic-electrical-wave testing system for the entire process of rupture instability in loaded coal. Simultaneous testing and analysis of multi-source physical information—including acoustic emission, ultrasonic wave velocity, and resistivity—were conducted during the damage process under uniaxial compression instability of different types of coal-rock samples. The aim was to explore the intrinsic relationships among precursor signals such as acoustic emission, wave velocity, and resistivity during coal rupture and instability. A precursor early warning model for coal-rock damage, based on the fusion of multi-source information, is proposed. The results indicate that the response pattern of acoustic emission signals reflects the initiation and development of internal damage deterioration in coal-rock samples. The characteristics of macroscopic wave velocity changes effectively capture the evolution of internal cracks and pores within the coal rock. The variation in apparent resistivity of the loaded coal mass corresponds well with its stress state, providing a reliable reflection of internal damage. The proposed model integrates precursor information such as acoustic emission, wave velocity, and resistivity into a BP neural network framework. By fusing multi-source data from acoustic emission, ultrasonic testing, and apparent resistivity, a precursor early-warning model for coal-rock damage and instability is established.</p>