<p>This study evaluates the effectiveness of five machine learning classifiers—Bayes Network, Logistic Model Tree, Decision Trees, Random Forest, and Sequential Minimal Optimization–Support Vector Machine (SMO-SVM)—for classifying biochemical oxygen demand (BOD) levels in wastewater. The models were trained using features extracted from visible and near-infrared (VIS–NIR) hyperspectral data. Prior to classification, robust principal component analysis (rPCA) was applied for dimensionality reduction. Six principal component scores, which collectively accounted for 99% of the explained variance, served as the input features for the algorithms. By comparing the models using the sum of ranking differences (SRD), we aim to identify the most accurate approach for classifying wastewater based on BOD values. After comparing the results of the output models with preprocessed data, the performance on wastewater samples was deemed acceptable. The Logistic Model Tree (LMT) and Random Forest (RF) models (SRD = 0) delivered the best performance, with overall classification accuracy (CCI) of 95.3% and 96.51%, respectively. Both models also exhibited excellent ROC areas (0.99), low error rates (MAE: 0.04 for LMT, 0.09 for RF), and strong balance between TP and FP rates, confirming their superior reliability. Additionally, BN, SMO–SVM, and DT, with SRD = 4, 8, and 12 respectively, and a probability of randomness falling between 3.32 × 10⁻⁶% and 9.12 × 10⁻⁵%, exhibit acceptable performance. These results underscore the potential of machine learning as a valuable tool for classifying BOD in wastewater management. </p> Graphical Abstract <p></p>