Operational Modal Analysis (OMA) is one of the most widely used approaches in Structural Health Monitoring (SHM) for extracting dynamic properties such as natural frequencies, mode shapes, and damping ratios from vibration data under operational conditions. The reliability of OMA outcomes is influenced by both the structural characteristics and the identification methods applied. This study investigates the efficacy of several OMA techniques, including Frequency Domain Decomposition (FDD), Enhanced Frequency Domain Decomposition (EFDD), covariance-driven and data-driven Stochastic Subspace Identification (SSI-COV, SSI-DATA), PolyMAX, and Ibrahim Time Domain (ITD), integrated within new built-on python library. The utility of this library is demonstrated on two structures: a controlled testbed of a three-story aluminium building excited by a shaker, and a real-world, multi-span bridge measuring 217 meters in length, featuring Gerber half-joints and equipped with mul tiple accelerometers. The bridge case study particularly highlights the library's capacity for precise modal parameter estimation in complex civil structures, alongside the practical benefits and limitations encountered. Additionally, the results obtained using the new built-on python library have been validated against ARTeMIS Modal, a widely recognized software for operational modal analysis, demonstrating consistency and reliability. This cross-validation further underscores the robustness of this library in diverse SHM applications. The findings of this research are intended to support researchers in selecting the most suitable OMA methods across diverse SHM applications, establishing this new built-on python library as a robust and versatile resource in the field of structural dynamics.