Bayesian dose–response models provide a flexible framework for estimating environmental biological impacts of increasing stressor concentrations (i.e. LD₅₀) while fully characterizing uncertainty. In this study, Bayesian binomial generalized linear models with logit and probit link functions were applied to a synthetic dose–response dataset. Posterior distributions for model parameters were obtained using CmdStanPy, and their structure was evaluated with a suite of artificial intelligence (AI)/machine learning (ML) techniques. Posterior samples were projected into reduced-dimensional space using Uniform Manifold Approximation and Projection (UMAP), analyzed for anomalies via Isolation Forest to detect latent structure, clustered with DBSCAN on α-β UMAP space, investigated for aggregations using k-means on β-LogLD50 space, and used to determine slope and intercept parameters estimated through the logit and probit frameworks. AI tools provided a way to see how the logit and probit models performed occupying distinct regions of parameter space and offered an additional way to monitor model behavior and convergence. More broadly, these methods demonstrate how AI-driven posterior analysis can improve model selection, uncertainty quantification, and communication in environmental and biological risk assessments.