Stress ribbon bridges (SRBs) create unique design challenges resulting from their extreme flexibility and the strong coupling between the forces in the cable and the deck. This paper presents the development of a Python-based interactive tool, to allow the rapid estimation of primary cable sag profiles in SRBs using traditional analytical formulations and artificial intelligence (AI). The tool combines parabolic cable sag equations, cable mechanics in catenary terms, load-span-sag-tension relationships, and a Machine Learning (ML) subcomponent. The AI module, including a Random Forest regressor, was initially trained using 103 real bridge data. The artificial intelligence module is fine tuned to the user through the iterative process of defining a task, predicting the sag, and then providing feedback to improve the regression module for the next analysis. The methodology is discussed, including derivations of the mathematical formulation of the traditional cable mechanics and design constraints. The paper describes the architecture of the AI module, key geometric and material inputs, the training process and the feedback and retraining workflow. Initial analysis shows a root mean square error up to 0.27 meter, which improved by approximately 33% to 0.18 meter after incorporating user feedback, successfully correcting systematic biases in various load cases. Tool illustrates the potential for machine learning to enhance structural engineering design by delivering fast sag estimates that adapt across user interactions, and will ultimately reduce the number of repetitive analysis cycles.