Cyanobacteria are microorganisms that thrive in terrestrial and aquatic environments. Flowers of cyanobacteria can produce blue toxins. These toxins pose a significant threat to the overall health of humans and animals alike, and their presence in dams affects the quality of drinking water for consumers, which is of concern to the authorities. When it comes to forecasting and monitoring, artificial intelligence and machine learning techniques are used to monitor potential toxic cyanobacteria concentrations in dams. Advance data processing is an essential step in machine learning, as it helps to improve model accuracy and the efficiency of the learning process. Techniques used in this study include multi linear regression (MLR), support vector regression (SVR), artificial neural network (ANN), and random forest regression (RFR). Enhancement technologies such as Boosting, bagging and Stacking improving predictive continuity and leveraging the collective power of weak models to achieve better accuracy, stronger generalization and reduced error ratio across different transformation scenarios. To assess the performance of these technologies, metrics are used to understand the accuracy of machine learning models predictions to concentrate cyanobacteria helping to improve strategies for controlling the spread of cyanobacteria. Combining these models can provide more accurate predictions and insights into the different factors affecting focus levels. These are smart strategies to address the complexity of environmental monitoring [1].