The global imperative for sustainable infrastructure necessitates a paradigm shift in civil engineering practices, moving from traditional methods to integrated, data-driven approaches. This review paper explores the confluence of Artificial Intelligence (AI) and Machine Learning (ML), financial optimization models, and advanced civil engineering materials, with a specific focus on the partial replacement of cement with fibers. The construction industry, a significant contributor to global carbon emissions, is under pressure to adopt sustainable materials like green concrete incorporating industrial by-products and fibers. However, the adoption of these novel materials is often hindered by uncertainties in long-term performance, lifecycle costs, and complex supply chain logistics. This paper argues that AI and ML serve as the critical enablers to bridge this gap. We review how AI/ML algorithms can predict the mechanical and durability properties of fiber-reinforced sustainable concrete, optimize mix designs for cost and performance, and inform digital twins for real-time structural health monitoring. Concurrently, we examine financial models including Life-Cycle Cost Analysis (LCCA), Monte Carlo simulations, and real options analysis that, when integrated with AI-driven insights, can de-risk investments in sustainable infrastructure. By synthesizing research from domains of material science, AI, and financial engineering, this paper provides a holistic framework for stakeholders to make informed decisions that align ecological responsibility with economic viability, thereby accelerating the development of resilient and sustainable infrastructure.