<p>Health literacy is a multidimensional construct essential for health decision-making, yet its computational assessment from naturalistic online discourse remains limited by categorical classifications that fail to capture its latent, continuous, and context-sensitive nature. To address this gap, we introduce an uncertainty-aware Bayesian deep learning framework that probabilistically infers latent health literacy from social media text while systematically quantifying predictive uncertainty. Using a large corpus of English-language health forum posts (<i>N</i> = 342&#xa0;k), we operationalized five theoretical dimensions of health literacy—Functional, Communicative, Critical, Digital, and Expressed—through validated NLP features. A Bayesian Variational Autoencoder with Monte Carlo Dropout models health literacy as a continuous latent variable and provides epistemic uncertainty estimates. The framework recovers a robust three-factor latent struc- ture: Core Integrated Proficiency (merging Critical, Communicative, and Expressed dimensions), Digital Proficiency (exhibiting an inverse association with Functional Literacy), and Applied Functional Literacy. The model achieves strong reconstruction performance (MSE = 0.109) with uncertainty estimates reliably correlated to prediction error (r = 0.438). From the continuous latent representations, we derive three distinct user profiles—Balanced, Specialized, and Transitional—revealing heterogeneous patterns of health literacy expression and adaptive communication behavior. This work advances computational health literacy assessment by providing a probabilistic, uncertainty-aware framework that moves beyond static categorization, with direct implications for personalized public health communication and hybrid human-AI assessment systems.</p>