Detecting damage in bridges that present signs of deterioration or have exceeded the expected lifespan is critical for ensuring safety in service. This paper suggests an approach for real-time damage detection for such bridges through monitoring and machine learning algorithms, which serve as timely alarms for decision-making and subsequent damage identification. The approach involves five steps: monitoring, data collection, data separation, feature extraction, and anomaly detection in real-time. Monitoring is ensured by strain gauges, accelerometers, and a temperature sensor. Data collection is ensured at high frequency continuously to capture the dynamic effects of loading. Data separation is provided to classify monitoring data according to loading events, which is in the case of the study characterized by the bridge opening, the bridge closing, and train passages. Feature extraction is provided to characterize monitoring data for each loading event. Anomaly detection is performed by the Isolation Forest and the One-Class Support Vector Machine algorithms. The algorithms are implemented in real-time for each new event. The approach is illustrated in a full-scale post-damage case study of a steel-bascule-railway bridge, in service since 1916, with signs of corrosion and fatigue. The results demonstrate the ability of the approach to capture a cracking event in real-time. The Isolation Forest algorithm is found to be more robust for damage detection compared to the One-Class Support Vector Machine. It assigned high scores to the events occurring during and after the cracking, highlighting its ability to capture such incidents promptly. These findings have significant implications for bridge owners as they can identify damage in components in real time, enabling them to take timely measures such as traffic interruption and subsequent repairs.