Dengue continues to be a major global health concern, disproportionately affecting vulnerable populations such as pregnant women and individuals with comorbidities. In this study, we propose a deterministic compartmental model that stratifies the human population into nine classes, including those with comorbid conditions and pregnancy, to capture the heterogeneities in disease transmission, progression, and outcomes. The model incorporates behavior-related exposure modifiers and hospitalization dynamics to reflect real-world complexities. We perform a thorough mathematical analysis of the model, including positivity, boundedness, and equilibrium analysis. Using a series of numerical experiments, we explore how variations in transmission rates, recovery rates, exposure risks, and hospitalization influence epidemic outcomes across subpopulations. The simulations reveal that comorbid individuals and pregnant women significantly alter the course and severity of dengue outbreaks, both directly and indirectly. Our findings underscore the importance of targeted interventions and subgroup-specific prevention strategies, offering novel insights to inform public health policy and dengue control programs.
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