In naval engineering, the study of damage caused by air-borne threats to naval structures has garnered significant attention due to its critical impact on structural integrity and safety, especially in combat ships. In mission-driven scenarios, rapidly and accurately identifying the exact location of such damage following extreme events is essential. To address this need, this paper presents a novel advancement in smart sensing and structural health monitoring (SHM) aimed at enhancing diagnostic capabilities [1]. This is achieved through the integration of the inverse finite element method (iFEM) [2], anomaly index formulations, and machine learning (ML) techniques. Additionally, high-fidelity computational models, including the finite element method (FEM), are utilized to simulate damage scenarios with precision. These simulations are then streamlined for efficient analysis and integration into the SHM framework. The proposed approach has been successfully applied to a real-world case study, addressing challenges such as optimal sensor placement, environmental constraints, and complex operational conditions that often impede accurate diagnostics. Ultimately, the paper introduces an enhanced iFEM-based strategy for damage detection and localization, supported by computational modeling, to advance real-time monitoring applications for large-scale, operational structures.