The present research presents an innovative strategy to improve water quality management in aquaculture environments by employing machine learning methods. A telemetric system was used to collect and monitor environmental data such as the power of Hydrogen (pH), Temperature, Turbidity and Total Dissolved Solids (TDS). The centrepiece of this study is the development of a telemetry-based predictive model, which leverages this data to predict changes in water quality. Several machine learning algorithms were tested, including Random Forest, Gradient Boosting, Polynomial Regression, Linear Regression, and k-Nearest Neighbors. After extensive testing, it was determined that Random Forest stood out as the most effective algorithm, achieving an extraordinary accuracy of 0.999 with a training time of just 12 s. This model allows for the proactive detection of conditions that could lead to the degradation of the aquatic habitat, enabling early warning of possible incidents. By anticipating these events, aquaculture resource managers can take corrective action in a timely manner, reducing the risks associated with water degradation. The results obtained underline the effectiveness and feasibility of applying machine learning methods in water quality management in aquaculture environments, showing that the combination of telemetry and appropriate algorithms can offer accurate and rapid solutions to improve sustainability and efficiency in this field.