Abstract: Deep resource exploitation and intelligent mine construction have placed higher demands on the refined characterization, efficient simulation, and reliable prediction of rock mechanics. Numerical modeling, grounded in explicit physical and mechanical principles, has become a core tool for rock mechanics analysis, and advances in numerical theory offer one of the most promising routes for addressing challenging problems in rock engineering. However, existing numerical methods are still limited by computational efficiency and engineering applicability, which restricts their in-depth application to complex rock engineering. The rapid development of artificial intelligence has sparked considerable interest in data-driven research methods in rock mechanics. Artificial intelligence can extract complex nonlinear relationships from experimental, monitoring, and simulation data, providing new tools for rock mass parameter inversion, failure mode identification, and engineering response prediction. However, its insufficient physical interpretability and strong dependence on large-scale, high-quality datasets remain inconsistent with the data scarcity and complex in-situ conditions of rock engineering. Therefore, integrating the strengths of numerical simulation and artificial intelligence to establish a synergistic framework driven by physical-mechanism constraints and data-driven learning has become an important direction for advancing rock mechanics. This paper systematically reviews recent advances in numerical simulation, artificial intelligence, and their integration in rock mechanics, clarifies the evolution from auxiliary complementarity and synergistic enhancement to digital-twin-based cyber-physical integration, and focuses on applications in cross-scale parameter identification, failure process simulation, hazard early warning, equipment optimization, and support design. Finally, common challenges in current integration methods are discussed, and future research directions are outlined with the aim of providing references for the safe and efficient construction of major rock engineering projects in coal and other energy sectors.