Blue-green algae are a major environmental concern in freshwater produce toxins and cause a wide range of problems including oxygen depletion, fish kills, harm or death to other aquatic organisms, and subsequent habitat loss. Cyanobacteria are a type of blue-green algae that form harmful algal blooms (HABs) in water ecosystems. In this study, artificial intelligence techniques, in particular artificial neural networks, were developed to estimate blue-green algae fluorescence for the year-round data collected in 2016-17 from western Lake Erie, USA. Based on the lake’s environmental conditions and available data, eight input parameters including phosphorous, nitrogen, chlorophyll-a, air temperature, water temperature, turbidity, wind speed, and pH were used to run the model. Five different learning algorithms were TESTED, and the Levenberg-Marquardt algorithm resulted in the highest R2 values of 0.98 and 0.72 for eight, and three (phosphorous, nitrogen, and chlorophyll-a) input parameters, respectively. Eight input parameters produced the best estimation approach.