Structural health monitoring (SHM) of infrastructure using sensor networks presents significant challenges, particularly for linear structures that require extensive coverage of critical hotspots. Among the various sensing technologies, optical fiber sensors have recently gained attention as a promising solution for distributed and long-span monitoring due to their ability to provide a high density of sensing points. However, the vast amounts of data generated by these sensors create substantial challenges in data handling, processing, management, and analysis. These challenges are further intensified under random loading and unknown conditions, where discerning patterns becomes particularly difficult. To address these issues, this study proposes a probabilistic-based framework for generating a health indicator (HI) through cumulative loading-time analysis of sensor data. The method reduces data dimensionality by calculating cumulative loading time within predefined windows and strain levels, thereby extracting meaningful features by fitting a cumulative distribution function. These features are then used to construct sensor-specific distributions, and Kullback–Leibler divergence is employed to monitor shifts between a trained baseline distribution and the current distribution. This produces the HI, enabling quantitative tracking of distribution shifts caused by structural changes or long-term anomalies. The proposed approach was validated through experimental fatigue tests, in which strain sensors monitored responses during fatigue progression. Results demonstrated the method’s effectiveness in detecting and localizing damage in two scenarios: when damage occurred directly at sensor locations and when it occurred nearby. Furthermore, the method was evaluated using both healthy-state field data and synthetic damage data generated from a fiber Bragg grating sensor network embedded in a roadway. This real-world scenario, characterized by random and unknown-magnitude loading, further validated the method’s robustness and applicability. Overall, the results demonstrate the potential of the proposed framework for practical deployment in SHM systems, offering efficient monitoring using large-scale sensor networks.