The textile industry is a significant contributor to industrial water pollution, particularly in developing nations like Bangladesh. Biofilters offer a cost-effective and environmentally friendly solution for treating textile wastewater, but their effectiveness largely depends on the appropriate selection based on pollutant characteristics. This study proposes a machine learning-based framework for predicting and recommending the most suitable biofilter technology—Compost Biofilter, Trickling Filter, or Advanced Bio-scrubber—using key wastewater parameters such as Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), heavy metal content, microbial load, temperature, and pH. A realistically simulated dataset was constructed using referenced pollutant ranges, and several supervised learning models were applied, including Decision Tree, Random Forest, Support Vector Machine (SVM), Gradient Boosting, XGBoost, and LightGBM. Evaluation metrics included accuracy, precision, recall, F1-score, ROC AUC, and k-fold cross-validation. Among all, LightGBM achieved the highest classification performance, with 98.50% accuracy. The proposed system demonstrates the potential of machine learning to serve as an intelligent decision support tool for real-time biofilter recommendation, helping improve efficiency and sustainability in industrial wastewater management.