The planning of new infrastructure corridors, such as roads, is a complex process with profound environmental, economic, and social consequences. Traditional planning methods often fail to holistically integrate geotechnical stability, environmental sensitivity, and long-term material sustainability. This paper presents a comprehensive review and proposes a novel geospatial and Artificial Intelligence (AI)-based decision support system (DSS) for planning sustainable infrastructure corridors. The core of the proposed DSS is a robust Geographic Information System (GIS) model that leverages Machine Learning (ML) techniques, specifically Frequency Ratio (FR) and Logistic Regression (LR), to create a suitability map by analysing multiple conditioning factors such as geology, slope, drainage density, land use, and environmental protected areas. Uniquely, this framework integrates material science by incorporating the optimized use of local industrial and agricultural waste (e.g., Fly Ash, Rice Husk Ash, Waste Glass Powder) in construction materials, thereby reducing the carbon footprint and promoting a circular economy. Furthermore, the review synthesizes how modern techniques, including geosynthetics for soil stabilization and robotics for precision construction, can be embedded within the planning process to enhance the resilience and minimize the environmental impact of the identified corridors. By synergizing advanced geospatial ML, sustainable material engineering, and automated construction technologies, this proposed DSS offers a transformative, multi-disciplinary methodology for building the next generation of resilient and environmentally responsible infrastructure.