Biological oxygen demand (BOD5) is an indicator used to monitor water quality. However, the standard process of measuring BOD5 is time consuming and could delay crucial mitigation works in the event of pollution. To solve this problem, this study employed multiple machine learning (ML) methods such as random forest (RF), support vector regression (SVR) and multilayer perceptron (MLP) to train a best model that can accurately predict the BOD5 values in water samples based on other physical and chemical properties of the water. The training parameters were optimized using genetic algorithm (GA) and feature selection was done using sequential feature selection (SFS) method. The proposed machine learning framework was firstly tested on the public dataset (Waterbase). MLP method produced the best model, with R2 score of 0.7672791942775417, relative MSE and relative MAE of approximately 15%. Feature importance calculations indicated that CODCr, Ammonium and Nitrate are features that highly correlates to BOD5. In the field study with a small private dataset consisting of water samples collected from two different lakes in Jiangsu Province of China, the trained model was found to have similar range of prediction error (around 15%), similar relative MAE (around 14%) and achieved about 6% better relative MSE.