This systematic literature review critically examines 162 articles on harmful algal bloom (HAB) modeling in riverine systems to uncover persistent gaps, redefine critical challenges, and propose trackable opportunities to advance future modeling efforts. Articles largely focused on site-specific applications (93%) across more than 80 rivers worldwide. Most modeled systems were large, eutrophic rivers with flow modifications or obstructions. Geographic clustering of modeled systems was pronounced, with South Korea accounting for 26% of articles, followed by Europe (25%), United States (21%), and China (12%). Modeling approaches were led by process-based models (59%), though use of data-driven models (37%) increased over time, reflecting advances in computing and monitoring technology. Modeling endpoints varied widely across the articles with many focused on gross measures of algal abundance and fewer representing more refined endpoints like algal toxins or community composition. Furthermore, inconsistent units and taxonomic resolution hindered comparability between models. Datasets used for model development and calibration typically spanned 5 years, with weekly to monthly sampling at 1–10 sites, though durations and site counts were positively skewed. Quantitative metrics of model skill were often absent and included a diverse set of metrics when reported. Across all models, nutrients, light availability, streamflow, algal physiological processes, and water temperature emerged as key predictors, though algal processes were rarely incorporated in data-driven models. Scenario analyses primarily were conducted with process-based models and addressed flow management, whereas forecasting applications were less common and typically used data-driven models. After almost 50 years of riverine HAB modeling, persistent challenges include underrepresentation of benthic habitats, neglect of side-channel and backwater influences, insufficient documentation of river features, and weak linkages between modeled endpoints and potential harms. Addressing these gaps through reporting of contextual information, models from other aquatic settings, benchmark datasets, and community-driven tools could advance riverine HAB modeling towards increased transferability and ultimately operational forecasts.