<p>The shift toward sustainable building and energy conservation necessitates intelligent, data-driven strategies for selecting construction materials. This study presents a metaheuristic-driven framework for optimizing sustainable material selection in energy-conscious infrastructure projects. Drawing from the Open Materials Database, the framework applies ten nature-inspired metaheuristic algorithms—such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimizer (GWO)—to solve a complex multi-objective problem: minimizing cost, embodied carbon, and environmental impact, while maximizing mechanical performance, durability, and recyclability. Results show the effectiveness of PSO and GWO in achieving good constraint satisfaction and convergence, while other algorithms provide various Pareto-optimal trade-offs. Visualization tools, such as radar charts and dynamic building material recommendation maps, allow for the flexibility and transparency of decisions. Low-carbon residential scenario illustration shows the applicability of the framework to a range of sustainability endpoints. The paper emphasizes the application of AI and machine learning in engineering to develop more sustainable and energy-efficient infrastructure. Future research entails integration with real-time decisions systems, Building Information Modeling (BIM), and uncertainty modeling to support practical application and resilience.</p>