Mountainous regions like Nepal and Northern India face profound infrastructure challenges driven by geological instability, seismic activity, harsh climates, and rapid urbanization. Conventional construction methods often prove inadequate, expensive, and environmentally unsustainable in these fragile terrains. This paper presents a comprehensive review advocating for a synergistic paradigm that integrates three key pillars for resilient infrastructure: (1) the Valorization of locally available natural and agro-industrial waste materials (e.g., rice husk ash, sugarcane bagasse ash, waste paper sludge ash) for soil stabilization and green concrete; (2) the strategic application of geosynthetics for reinforcement, slope stabilization, and road construction; and (3) the deployment of Intelligent Transport Systems (ITS) for real-time traffic and hazard management. Unifying these pillars is a robust framework of Geographic Information Systems (GIS), remote sensing, and advanced Machine Learning (ML) techniques, including Frequency Ratio, Weight of Evidence, Logistic Regression, and Artificial Neural Networks. These tools are critically reviewed for their application in multi-hazard susceptibility mapping, material supply chain optimization, and predictive infrastructure monitoring. By synthesizing existing research and proposing an integrated model, this review demonstrates that a data-driven, circular economy approach can significantly enhance the sustainability, resilience, and cost-effectiveness of infrastructure development in the world's most vulnerable landscapes.