Road networks support economic activities, emergency services, and daily operations. However, those located in complex terrains, such as hillside regions, are particularly vulnerable to natural hazards such as earthquakes, landslides, and wildfires, which can severely disrupt connectivity and cause significant social and economic impacts. As climate change accelerates the frequency and intensity of these hazards, there is a growing need for scalable, data-driven approaches to enhance the resilience of these networks. Traditional methods for planning and retrofitting infrastructure fall short in addressing the complexities posed by both terrain and hazard exposure, particularly in large-scale networks.This dissertation aims to develop a comprehensive, data-driven framework with multiple modular components to improve the resilience and capacity of transportation networks in vulnerable regions. It focuses on introducing scalable algorithms for ranking road criticality, integrating hyperlocal data into capacity expansion planning, and optimizing retrofitting strategies to ensure network functionality during and after disaster events. Additionally, the work incorporates an equity dimension in retrofitting strategies to ensure fair resource distribution across different socioeconomic groups. To achieve these objectives, this dissertation presents multiple frameworks that develop and apply advanced machine learning and optimization techniques. First, it introduces a Graph Neural Network (GNN) for ranking road segments based on their criticality. This approach provides a more efficient alternative to conventional methods, significantly reducing the computational time needed to rank road segments while maintaining high accuracy. This capability enables rapid decision-making in both everyday operations and during disruptions. Second, the dissertation focuses on integrating hyperlocal data into capacity expansion planning, utilizing data gathered through mobile mapping systems equipped with LiDAR and vision-based technologies. By incorporating this high-resolution data, the framework allows for more accurate capital allocation, especially in hillside regions where terrain complexity poses a challenge for traditional methods. Third, the dissertation addresses the challenge of optimizing earthquake resilience in large-scale networks. A probabilistic framework is developed that combines a Siamese Graph Convolutional Network with a Genetic Algorithm. This approach evaluates various retrofitting strategies and identifies optimal solutions that balance budget constraints while minimizing welfare losses for low-income populations, ensuring a more equitable distribution of resources. Lastly, a novel framework is developed for managing wildfire risks to road networks using Generative Adversarial Networks (GANs). It generates high-fidelity synthetic weather scenarios that influence wildfire ignition and spread, creating a stochastic catalog of events to simulate fire progression and its impacts on road networks. This information feeds into an optimization model that identifies critical road segments for retrofitting, optimizing investments to maximize resilience. The methodologies were applied to transportation networks, mainly focusing on the hillside regions of Los Angeles, showcasing the effectiveness of each approach. The proposed GNN models improved the speed of road criticality rankings, while maintaining high accuracy and enabled large-scale retrofitting optimization. Hyperlocal data integration proved essential for precise capacity expansion, while the earthquake and wildfire resilience frameworks optimized retrofitting strategies, significantly increasing network resilience even with small investments. The main contribution of this dissertation is the development of scalable, data-driven methods for enhancing the resilience of transportation networks. By integrating advanced machine learning models and hyperlocal data, the proposed approaches provide a robust toolkit for policymakers and infrastructure planners to ensure that transportation networks can better withstand and recover from disruptions. These contributions form a strong foundation for resilient and more equitable infrastructure planning in regions susceptible to natural hazards.