The construction industry stands at a critical juncture, facing dual imperatives of enhancing resilience while radically reducing its environmental footprint. Traditional approaches to sustainable construction have often been fragmented, focusing either on material-level innovations or project-level efficiency. This paper presents a comprehensive review of an integrated framework that synergizes Life-Cycle Assessment (LCA) with Digital Twin technology to revolutionize eco-friendly construction practices. The core of this framework is a dynamic, data-driven digital replica of construction projects that simulates long-term performance and environmental impacts of utilizing recycled materials including various ashes (Fly Ash, Rice Husk Ash, Sugarcane Bagasse Ash), Waste Glass Powder, and fibers alongside advanced methods like geosynthetics, Fiber-Reinforced Polymers (FRP), and robotics. By incorporating machine learning algorithms such as Artificial Neural Networks (ANN), Logistic Regression, and Frequency Ratio with geospatial data from Remote Sensing and GIS, the digital twin evolves from a static model to a predictive, self-learning system. This review systematically analyzes how such integration enables real-time monitoring, predictive maintenance, and continuous optimization of resource utilization across the entire building lifecycle. The paper further explores how Information Value methods and Weight of Evidence can enhance decision-making processes for material selection and construction methodologies. Findings indicate that the proposed digital twin framework can reduce carbon emissions by 30-40%, improve material efficiency by 25%, and extend structure lifespan by 20-30% through proactive maintenance strategies. This paradigm shift toward data-driven, sustainable construction represents a significant advancement in achieving the United Nations Sustainable Development Goals (SDGs) 9, 11, and 13, while offering substantial economic benefits through optimized life-cycle costs.