Physics-based simulations, while central to transmission tower fragility analysis, are often computationally prohibitive for system-level or regional-scale assessments, where the objective is to evaluate the performance of multiple transmission towers with diverse geometries over large geographic regions. The study addresses these challenges by developing a generalized surrogate wind fragility modeling framework. This framework facilitates rapid and comprehensive vulnerability assessment of transmission towers by integrating structural design, fragility analysis, and deep-learning-based surrogate modeling. This integration allows the framework to explicitly account for site-specific environmental conditions and tower-specific design characteristics across diverse geographic regions in the United States, thereby enhancing its generalizability and applicability to a wide range of tower designs and locations. Moreover, contrary to traditional fragility models, the framework considers a more realistic representation of extreme wind events, where multiple hazard variables (i.e., wind speed, direction, and rainfall intensity) influence tower fragility concurrently. The resulting surrogate models provided high prediction accuracies. For unseen towers, the models achieved a mean square error of 0.0202 and an R 2 of 0.899 and demonstrated consistency with conventional physics-based fragility models. Moreover, the inclusion of location-specific design parameters enabled the models to adapt to different regional performance objectives, while considering tower-specific parameters in the design phase facilitated rapid vulnerability assessments for various tower designs within the transmission network. Overall, the proposed framework can potentially reinforce decision-making for grid resilience planning by offering a practical and computationally efficient solution for system-level vulnerability analysis of transmission tower networks. • The framework combines design and fragility processes for model generalizability. • The interaction of three environmental parameters is considered in tower fragility. • The developed surrogate models provide a high prediction accuracy. • Surrogate models can be generalized for various tower configurations and locations. • The framework provides a computationally-efficient solution for network-level analysis.