<p>The design, building, and maintenance of underground structures are being revolutionized by applying Artificial Intelligence (AI) techniques in tunnel engineering, which provide previously unheard-of levels of safety, efficiency, and precision. This scientific investigation looks at the various ways AI may help with the intricate problems of tunnel engineering, especially in geotechnical analysis, construction automation, and long-term performance monitoring. The interpretation of large amounts of geological data, precise subsurface condition forecasts, and design uncertainty reduction are all made possible by AI-powered machine learning algorithms. By improving cutterhead performance, decreasing wear, and limiting downtime, AI enhances the operation of tunnel boring machines during tunnel construction. Real-time monitoring systems powered by AI enable proactive risk management and raise safety standards by detecting irregularities like water intrusion and ground deformations. Additionally, to extend the lifespan of tunnel infrastructure, AI-based predictive maintenance systems examine structural health data to foresee breakdowns and plan prompt repairs. Automation in excavation and lining installation has also been made possible using sophisticated computer vision and robots, decreasing the need for human involvement in dangerous situations. Notwithstanding these developments, many obstacles remain, including the inability to obtain high-quality data, computational complexity, and incorporating AI models into conventional engineering processes. This study highlights essential applications, summarizes recent research, and talks about the potential and limitations of artificial intelligence in tunnel engineering. The results highlight how AI can transform tunneling by promoting safer, more economical, and sustainable engineering methods. Furthermore, this paper proposes new portals to expand the study of AI applications in tunnel engineering, which would benefit academics, engineers, and tunnel designers.</p>