This study introduces the development of a comprehensive parametric 3D model of the Colle Castino Bridge using OpenSeesPy, designed to facilitate extensive statistical analyses via Monte Carlo simulations. Structural engineering challenges, such as uncertainties in material properties, geometric dimensions, and loading conditions, are addressed by parameterizing all aspects of the bridge's geometry and materials. This allows for the efficient simulation of numerous scenarios, enabling robust statistical evaluations. The parametric model systematically varies input parameters, including cross-sectional geometry, reinforcement details, and material properties, to simulate diverse conditions such as corrosion and material degradation. This methodology quantifies uncertainties, assesses their impact on structural performance, and evaluates reliability by calculating failure probabilities under different scenarios. The study provides valuable insights for data-driven maintenance and retrofitting strategies aimed at enhancing bridge safety and longevity. The model’s development emphasizes modularity, organizing material properties, geometry, and structural elements into classes and functions, enabling flexible adjustments and updates. This research demonstrates a systematic approach to parametric modeling and statistical analysis of complex structures, contributing to risk-based decision-making in structural engineering. By improving the understanding of uncertainty effects on performance, the study advances the design of resilient, reliable bridge structures and supports informed maintenance strategies.