Abstract: With the continuous growth of energy demand and the gradual depletion of shallow coal resources, coal mining is increasingly extended to greater depths. Deep coal seams are generally characterized by high ground stress, strong gas adsorption, and low permeability, while their geological conditions become more complex. As a result, the gestation process of coal and gas outburst is governed by stronger multi-factor coupling and greater suddenness, and its prediction becomes substantially more difficult. To improve the accuracy and stability of risk identification, a systematic review method is adopted to summarize the representative mechanism hypotheses and evolutionary processes of coal and gas outburst. The key causative mechanisms associated with gas pressure, gas content, ground stress, coal structure, and mining disturbance are reviewed, and the multi-field coupling characteristics involved in the formation process of coal and gas outburst are analyzed. On this basis, the application of machine learning in coal and gas outburst prediction is comprehensively summarized. The research progress of discriminant analysis, clustering analysis, decision trees, support vector machines, artificial neural networks, extreme learning machines, and ensemble learning is systematically reviewed. The advantages and limitations of these methods are compared in terms of interpretability, nonlinear fitting capability, sensitivity to data quality, and adaptability to complex geological conditions. The results indicate that discriminant analysis and clustering analysis are suitable for scenarios with limited samples and pattern discovery, but their anti-interference capability remains insufficient. Decision trees exhibit good interpretability and facilitate the extraction of risk discrimination rules, whereas they are prone to overfitting. Support vector machines show stable performance under small-sample, high-dimensional, and nonlinear conditions. Neural-network-based methods possess strong fitting capability and are able to capture multi-factor coupling relationships, but their interpretability is limited and their performance depends heavily on high-quality samples. Extreme learning machines are characterized by high training efficiency and are suitable for rapid modeling, although their generalization capability still requires further improvement. By integrating multiple base learners, ensemble learning enhances robustness and generalization capability and demonstrates superior stability and adaptability in cross-mine prediction and complex application scenarios. It is therefore regarded as a key direction for future research and engineering application. Accordingly, an ensemble-learning-centered research framework is developed, in which mechanism constraints are incorporated into model construction, multi-source data fusion is promoted in combination with intelligent algorithms, and interpretability analysis and dynamic updating mechanisms are integrated to improve the reliability and practical applicability of prediction results. The conclusions show that the development of ensemble learning and mechanism–data fusion strategies helps overcome the limitations of existing models in generalization and stability. It also provides methodological support for the construction of cross-regional, transferable, and interpretable intelligent prediction platforms for coal and gas outburst, thereby contributing to proactive mine-disaster prevention and intrinsic safety assurance.