Because cyanoHABs are a concern for health throughout the United States, there is a need for a large-scale prediction model. To address this need, we used a Bayesian model to estimate the probability of a cyanobacterial bloom occurring in a given week. Specifically, we used an Integrated Nested Laplace Approximation (INLA) model because it provided a complex and computationally efficient forecasting model that can work with missing data and irregular lake sampling. The INLA model used cyanobacteria presence data derived from Sentinel-3 Ocean Land Color Instrument along with environmental predictor variables from 2016 to 2022 in satellite resolvable lakes across CONUS. This model was applied to forecast World Health Organization recreation Alert Level 1 exceedance >12 μg L−1 chlorophyll-a with cyanobacteria dominance for 2192 satellite resolved lakes in the United States. The prediction results were compared to independent previous cyanobacteria presence satellite imagery to provide performance statistics.