The global construction industry, a cornerstone of modern civilization, faces an existential challenge: to reconcile its immense consumption of natural resources and significant environmental footprint with the urgent need for sustainable development. A promising pathway lies in the large-scale integration of recycled materials, such as recycled concrete aggregate (RCA), reclaimed asphalt pavement (RAP), and industrial by-products like fly ash and slag, into new asphalt and concrete. However, this integration is fraught with technical complexity and economic uncertainty. Traditional mixture design methods are often iterative, time-consuming, and fail to holistically account for long-term environmental and financial performance. This review paper posits that a paradigm shift is underway, driven by the convergence of Lifecycle Assessment (LCA) and Artificial Intelligence (AI) and Machine Learning (ML) models. We explore how this synergy creates an intelligent, data-driven framework for designing sustainable asphalt and concrete mixtures. The paper systematically reviews the application of AI/ML models from fundamental regression analysis to advanced deep learning and multi-objective optimization in predicting material properties and optimizing mixture designs incorporating high volumes of recycled content. Crucially, it extends the discussion beyond technical performance to integrate LCA findings and financial viability, translating material science into the language of business management and finance. By examining the entire value chain from material sourcing and production to construction and end-of-life this review demonstrates how AI-powered tools can empower decision-makers to select mixture designs that are not only mechanically sound but also minimize environmental impact (e.g., carbon footprint, energy use) and maximize economic return, thereby paving the way for a truly circular and profitable construction economy.