Abstract: Coal and gas outbursts remain one of the major hazards in deep coal mining in China. With increasing mining depth and intensity, the associated influencing factors and their coupling relationships have become increasingly complex, and the combined effects of high in-situ stress, gas pressure, and gas content markedly increase outburst risk, seriously constraining safe and efficient coal production. Accurate and reliable monitoring and early-warning technologies and equipment have therefore become an urgent necessity for monitoring and preventing outburst dynamic hazards in deep coal mines. A theoretical model of acoustic and electromagnetic effects induced by the failure of gas-bearing coal under stress was researched, forming the foundation for a multi-signal early warning mechanism based on acoustic, electromagnetic, and gas responses. The response characteristics of acoustic, electromagnetic, and gas signals during outburst evolution were clarified, and an intelligent acoustic-electromagnetic-gas monitoring and early-warning equipment and system were developed. Distributed monitoring methods for acoustic-electromagnetic-gas signals in both roadway development and longwall extraction faces were formulated. The precursor characteristics of acoustic, electromagnetic, and gas signals during the disaster evolution process were analyzed, and an intelligent recognition method for these precursors was established. The time–frequency variation patterns and complex evolutionary characteristics of precursor responses and other disturbance signals during outburst development were further investigated. A comprehensive technical system for acoustic-electromagnetic-gas monitoring and early warning was constructed, along with a hierarchical multi-signal fusion warning approach. A real-time cloud platform was also developed for the integration and sharing of monitoring and warning data. Typical field cases were selected to validate the proposed intelligent acoustic-electromagnetic-gas monitoring and early-warning method in engineering applications. Field validation confirmed that intelligent and accurate identification and early warning of coal and gas outbursts can be achieved through the deployment of specialized equipment, data integration, and comprehensive analysis of signal features and coupling relationships. This work provides theoretical and technical support for the prevention, control, and engineering-scale application of coal and gas outburst hazards in deep coal mines.