Objective: The development of modern urban agglomerations requires cultivating integrated transportation networks, advancing transportation integration, and building resilient, comprehensive three-dimensional systems. Building a highly efficient and stable transportation network is fundamental to promoting the growth of modern metropolitan areas. Methods: This paper focuses on the metropolitan area multimodal transportation network (MA-MTN) and uses complex network methodologies to model different transportation network construction processes. By applying a load capacity model, it defines the load and capacity levels of the MA-MTN and develops a load redistribution model to facilitate network operations; then, different dimensions of network resilience evaluation indicators were developed, focusing on three levels: overall network, network structure, and network function. From an integrated perspective, a comprehensive resilience indicator for multimodal transportation networks in urban agglomerations was established. The Xi'an metropolitan area was selected as the research subject to conduct simulation analyses of the multimodal transportation network's integrated resilience. The sensitivity levels of different traffic subnetworks in the multimodal transportation network of the metropolitan area were assessed through scenario simulation methods. Simulations examined the performance loss stage, the recovery stage following the implementation of recovery strategies, and the dynamic recovery stage during network failures caused by attacks on the multimodal transportation network. The effectiveness of strategies aimed at improving the integrated resilience of the metropolitan multimodal transportation network was also compared and analyzed. Results: The research results indicated that different transportation subnets play distinct roles in the multimodal transportation network of urban agglomerations. When MA-MTN is attacked, the sharpest decline in network performance occurs before the failure rate of network nodes reaches 60%. The node capacity adjustment parameter β in MA-MTN needs to be dynamically calibrated, with a value of 0.2 recommended for daily operations and 0.7 during extreme weather events or holiday peaks. Enhancing the resilience of urban multimodal transportation networks requires prioritizing transportation efficiency factors in the transfer of passenger flow transfer between nodes; this approach improves the comprehensiveness of the network resilience. Recovery strategies based on node load capacity levels are the most effective in improving the integrated resilience level of these networks. Such strategies enhance resistance to attacks and significantly improve recovery capabilities during cascading failures, ensuring robust network performance even under dynamic recovery conditions. Conclusions: This paper measures the integrated resilience level of transportation networks against external disturbances using a multidimensional integrated perspective; moreover, it identifies effective strategies to improve the structural and functional resilience of multimodal transportation networks in urban areas. Based on the research results, this study emphasizes the importance of determining critical thresholds for network node failures. It recommends dynamically adjusting the node capacity parameter β according to the operational conditions of the MA-MTN and external factors. Furthermore, it advocates optimizing the transfer process of node passenger flow load by prioritizing transportation efficiency during network operations. The study also highlights the significance of prioritizing node load capacity and developing effective recovery strategies based on the comprehensive importance of nodes. These approaches are crucial for promoting the construction of efficient, stable, and multimodal transportation networks in urban areas.