This study presents a novel, open-source framework designed to enable radiological accident consequence analysis and environmental monitoring optimization for advanced nuclear reactors. The developed toolset integrates a comprehensive source term database, aggregated from 24 publications and covering 19 unique reactor designs, with a Python-based wrapper for the HYSPLIT atmospheric transport model to simulate radionuclide dispersion and calculate offsite dose consequences. In addition, we implement an algorithm for optimizing radiation sensor network placement based on the Gaussian process regression. To demonstrate the framework’s capabilities, we applied it to a proposed 5 MWe microreactor at the Eielson Air Force Base in Alaska, simulating limiting accident scenarios with source terms derived from five distinct reactor concepts using 2023 meteorological data. The demonstration estimated a mean effective dose below the EPA’s 10 mSv advisory limit for evacuation at the site boundary (1 km), with peak doses at the nearby population center of Fairbanks (~35 km) comparable to natural fluctuation in the background radiation. At the same time, the analysis reveals that accident consequences can vary by up to five orders of magnitude depending on reactor design, highlighting the importance of reactor-specific assessments. Overall, the proposed toolset enables transparent, community-driven risk assessment to support informed decision-making in the siting and safety evaluation of nuclear power plants.