Deep learning is a powerful data-driven approach. That profoundly transforms seismological research. Its end-to-end modeling paradigm, automated deep feature extraction capability, and strong ability to fit complex nonlinear relationships effectively overcome the limitations of traditional methods that rely on manual feature engineering, empirical thresholds, and specific physical models. It has become a frontier core technology in seismic signal processing. A systematic review framework for the application of deep learning in seismic signal processing was constructed. Key advances in real-time seismic signal analysis, seismic event characterization and source parameter inversion, as well as seismic physical understanding and disaster risk assessment were comprehensively summa- rized. An innovative coupling perspective of “ data-model-physics-task-evaluation” was introduced. The integration pathways between emerging paradigms and seismic signal processing were explored. The results demonstrate that this structured framework clarifies the in- ternal logical relationships among data representation, model design, physical constraints, task objectives, and evaluation metrics. It provides a technical roadmap and theoretical reference for building more robust, interpretable, and low-dependence next-generation in- telligent seismic monitoring systems. It promotes the development of seismology toward automation, real-time processing, and intelli- gence, and shows significant academic significance and practical value.