Nutrient loading and eutrophication in coastal waters are the causes of water quality degradation and loss of marine biota, which has led to ecological imbalance. Understanding and modeling the level of eutrophication as a function of environmental parameters can be beneficial to coastal ecosystem management. The limitation of deterministic and empirical models in accurately predicting the level of algal blooms, and the nonlinear relationship between the water quality and environmental parameters and that of the level of chlorophyll a necessitate a new approach using machine learning and data-driven modeling. A multilayer perceptron-back propagation (MLP-BP) algorithm of artificial neural network (ANN) was used to predict the level of eutrophication (chlorophyll a) from water quality parameters monitored at two Florida Bay water quality monitoring stations (FLAB03 and FLAB14). Based on the correlation of monthly nutrients (total phosphate, nitrite, ammonium) and other water data (temperature, turbidity, and dissolved oxygen) to the level of chlorophyll a, an input-output data structure was selected. Seven input data scenarios were studied, and model performance was compared using four indices. Monthly data from 1992 to 2004 were partitioned into training and testing subsets. Results show that chlorophyll a was predicted well with the selected inputs, with an average R² and model efficiency (E) of 0.856 and 0.582, respectively. Prediction with antecedent chlorophyll a alone gave a stable result with smaller error and higher performance attributed to easier and more efficient training. It was also found that ANN performed better at FLA03 than at FLA14. It is shown that the MLP-BP technique is applicable to the monitoring and prediction of algal blooms and will be crucial to coastal watershed management.