Coastal intermodal freight networks (CIFNs)—comprising maritime, roadway, and railway corridors—are vital to the economies of coastal communities. These networks facilitate efficient cargo transfers, thereby underpinning supply chains. However, they are increasingly vulnerable to disruptions caused by extreme coastal hazards, which are being intensified by climate change and population growth. Such disruptions, as evidenced by past hurricane events, can have devastating effects, with impacts rippling through communities and leading to both short- and long-term socio-economic consequences. Given these challenges, it is essential to model the recovery and resilience of CIFNs to better assess their capacity to restore cargo-transfer functionality and to inform planning efforts aimed at enhancing this capability. Despite the importance of this task, current modeling methods have significant limitations. These include a lack of consideration for multiple commodity types, high computational complexity, limited ability to accommodate stakeholders at various scales, and a failure to effectively incorporate resource-related constraints into the recovery modeling process. This study aims to address these gaps by proposing a multi-scale probabilistic resilience modeling framework tailored to the intricacies and complexity of CIFNs. Specifically, this framework enables a comprehensive characterization of the intermodal network at the component, link, and network levels. It integrates both roadway and railway corridors with port terminals through specially adapted network flow models, enabling the estimation of network-level functionality while accounting for multiple shipments, diverse commodities, and various stakeholders. In addition to posing the overarching framework for resilience modeling of CIFNs, this study addresses a critical gap in modeling flow-based port functionality under disrupted conditions—a key component in simulating the intermodal recovery of post-disaster satisfied demands between maritime and inland corridors. Specifically, this model incorporates the probabilistic availability of port structural and handling components following a disaster, enabling more detailed, physics-based estimations of flow capacity at the terminal level, extending beyond just the handling capacity of cargo at berth areas. Another critical gap this study aims to bridge is the lack of efficient methods for incorporating resource constraints into the modeling of CIFN recovery following extreme coastal hazards. To address this, the study introduces a resource-aware framework that not only considers the available resources for restoration—both monetary and labor—but also how these resources are allocated by decision-makers under post-disaster strained conditions. Specifically, two allocation algorithms are proposed: an optimization-based approach and a heuristics-based approach. These algorithms are designed to explore the influence of the interplay between different conditions of resource availability and allocation efficiency on network-level resilience outcomes. The proposed models and methodologies are demonstrated through three real-world case studies in Mobile and Baldwin counties, Alabama, involving a multi-commodity port site, a railway network, and a fully integrated CIFN that incorporates these systems along with roadway corridors. These case studies assess post-disaster functionality at both local scales (e.g., recovery of port flow capacity) and regional scales (e.g., restoration of satisfied demands across the intermodal network). These resilience analyses are conducted under various hurricane and resource constraint scenarios. Ultimately, the risk-informed, resource-sensitive resilience assessments developed and demonstrated in this study are aimed at supporting broader community resilience enhancement efforts, which have gained critical importance in light of escalating climate-driven disasters. Notably, this work is part of a multidisciplinary effort supported by the NIST Center for Risk-Based Community Resilience Planning. IN-CORE—a key outcome of this initiative—is an open-source suite of tools for community resilience modeling, which will incorporate the data compiled and methodologies proposed in this work, thereby expanding its potential to contribute towards bolstering community resilience in the face of extreme coastal events.