<p>This study proposes a hybrid methodology integrating Artificial Neural Networks (ANN) with the Taguchi method and Teaching–Learning-Based Optimization (TLBO) for predicting Fe(III) ion extraction efficiency in biosorption processes. The Taguchi approach is employed to systematically optimize critical ANN hyperparameters, including the number of neurons, learning rate, training algorithm, and training ratio, while TLBO is used to refine the network weights and biases, enhancing predictive performance. Experimental data from a Box-Behnken design, covering variables such as initial Fe(III) concentration, contact time, stirring speed, temperature, and biosorbent dosage, were used to train the model. The proposed hybrid framework combines experimental design principles with machine learning optimization to achieve robust, reliable, and computationally efficient predictions. This approach provides a structured methodology for modeling heavy metal ion removal and demonstrates a novel integration of ANN with dual optimization strategies, offering practical insights for industrial biosorption applications.</p>