The transformation of rock tunnel engineering (RTE), a pivotal component of modern infrastructure, is driven by advances in signal processing and artificial intelligence (AI). This review synthesizes the existing literature to examine how time‐series, image, and manually collected data are acquired, preprocessed, and utilized in RTE. These data are applied to various aspects, including geotechnical property analysis, construction optimization, safety assurance, and operational maintenance optimization. We summarize the core algorithms and identify prevailing challenges such as data heterogeneity and data silos. Future research should focus on multi‐source data fusion and large models, while microseismic‐based rock‐burst prediction also warrants further exploration. Overall, the fusion of signal processing and AI significantly enhances the efficiency, accuracy, and safety of tunnel projects, offering a robust pathway towards sustainable next‐generation underground infrastructure.
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Zhao et al. (2026) studied this question.
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