The global infrastructure sector, particularly road construction, faces a dual challenge: escalating costs and significant environmental degradation. This review paper explores the synergistic integration of waste fibers (WF) from industrial and agricultural by-products and engineered geosynthetics as a sustainable alternative in pavement construction. While the technical benefits of these materials, such as improved soil stabilization, crack resistance, and drainage, are increasingly documented, their widespread adoption is hindered by perceived financial risks and a lack of robust, long-term economic data. This paper systematically reviews the existing literature to construct a comprehensive financial viability model for WF-geosynthetic composites. It moves beyond traditional Life-Cycle Cost Analysis (LCCA) by critically examining the role of advanced Artificial Intelligence (AI) and Machine Learning (ML) methods in de-risking these sustainable investments. We analyze how AI/ML can optimize material design, predict long-term performance, and automate asset management, thereby transforming uncertain cost projections into data-driven financial forecasts. The review identifies that the initial premium of 10-25% for integrating these materials is often offset by a 30-50% reduction in maintenance cycles and a 15-40% extension in service life, leading to a positive Net Present Value (NPV) and attractive Life-Cycle Cost (LCC) savings. The paper concludes that the fusion of sustainable material science with AI-driven predictive analytics presents a paradigm shift, making green road construction not just an environmental imperative but a financially superior strategy for long-term asset management.