This study presents an integrated framework for developing a structural digital twin, combining real-time structural response prediction with machine learning–based damage identification. The framework has been validated using the well-documented KW-51 railway bridge in Belgium and is currently being implemented for the development of a digital twin of the Mussafah Bridge, United Arab Emirates. The proposed methodology consists of two main components: structural response prediction and damage identification. Existing machine learning applications in structural engineering primarily focus on damage detection and classification, while replication of full-field structural responses at finite element resolution remains largely unexplored. The first component addresses this gap by predicting governing structural response variables across the entire structure without requiring repetitive FE analyses. This capability is essential for enabling real-time response prediction within a digital twin environment. The framework employs physics-informed Deep Operator Networks, guided by structural balance laws, to achieve near-instantaneous prediction of static structural responses. Hybrid loss functions incorporating structural matrices enable prediction errors below 5% while significantly reducing computational cost. The second component focuses on damage identification, a critical aspect of structural health monitoring. Unlike conventional approaches that rely solely on either long-term monitoring data or numerical simulations, the proposed method integrates both field measurements and FE-generated data. Signal processing techniques, combined with modal and acceleration-based inputs, are used alongside advanced machine learning models, including stacked GRU networks, kNN classifiers, and CNNs, to detect damage existence, localization, and magnitude with high accuracy using limited monitoring data. By integrating physics-informed response prediction and data-driven damage identification, this research establishes an efficient pathway toward automated, real-time digital twins for bridge structures. The ongoing implementation of the Mussafah Bridge demonstrates the practical applicability of the framework for real-world infrastructure monitoring and decision support