As the integration of signal processing and machine learning deepens, this interdisciplinary field is experiencing rapid advancements. The emergence of deep learning has significantly enhanced the automation and intelligence of signal analysis, achieving remarkable breakthroughs in domains such as audio processing, image recognition, and medical diagnostics. Simultaneously, multimodal data fusion and AI-driven signal analysis have emerged as critical trends. However, challenges such as limited model interpretability, robustness against noise and distributional shifts, and adaptability in resource-constrained environments remain pressing issues, necessitating further research and practical exploration.