<p>Vehicular collisions with bridges pose significant risks to transportation infrastructure and public safety. Recent studies deployed bridge datasets and machine learning tools to present models that assess and highlight bridges with high-risk metrics. This paper presents the development of a Python-based software platform that enables interactive, data-driven assessment of bridge-vehicle collision risks through integrated visualization and modeling. The tool supports multiple analytical approaches, including rule-based logic, machine learning algorithms, fuzzy inference systems, and hybrid models that combine strengths of each. The platform features an interactive graphical interface with geographic mapping, dynamic data filtering, and integration with external mapping services such as Google Maps. Results can be exported for further engineering analysis or reporting. Designed for use by structural engineers, transportation planners, and infrastructure authorities, the software aims to support prioritization efforts for bridge protection, retrofitting, and policy development. The software architecture allows for future model extension and dataset expansion, making it adaptable to diverse transportation systems and evolving risk metrics. This paper discusses the design methodology, implementation framework, modeling strategy, and example applications using state-level bridge inventory data. The prediction models along with the platform represent an edge-cutting tool to predict and highlight vulnerable bridges subject to vehicle collision risks. This enables stakeholders and the transportation community to make informed decisions on further investigation and maintenance. Thus, achieving the safety and sustainability of bridge infrastructures.</p>