As climate change intensifies, infrastructures worldwide face unprecedented risks from extreme weather events, rising temperatures, and unpredictable environmental stressors. Traditional structural engineering approaches are no longer sufficient to ensure resilience and safety in this evolving context. Climate-resilient structural engineering leverages sensor networks, machine learning algorithms, and predictive maintenance models to design, monitor, and adapt infrastructures in real time. By integrating smart technologies, structural systems can anticipate failures, optimize resource allocation, and extend their service life while ensuring safety under climate-induced stresses. This paper explores the convergence of advanced sensor technologies, artificial intelligence, and predictive engineering practices in creating sustainable, resilient infrastructures. It highlights the transformative role of data-driven models in mitigating risks, reducing economic losses, and safeguarding communities in the face of climate uncertainty.