Empirical and machine learning models are estimation tools relevant to obtaining scalable solutions to engineering problems. In this study, response surface methodology (RSM) was incorporated to correlate the experimental findings based on mathematical models. Artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) were the artificial intelligence tools used to create trainable algorithms. Feed data consolidated hydration temperature (50 to 90 °C), hydration time (3 to 7 hours), sulphation temperature (120 to 160 °C), diatomite to hydrated lime ratio (0 to 1) and inlet gas concentration (500 to 2500 ppm) as the independent variables mapped against sulphur capture capacity (Y₁ - 5 to 54 %) and reagent utilization (Y₂ - 4 to 42 %) as the dependent variables. The model accuracy and cost analysis were determined using the statistical error analysis tools including root mean square (RMSE), mean square error (MSE) and coefficient of determination (R²). The ANN models presented more acceptable and reliable data estimation with R² values greater than 99% compared to the RSM and ANFIS models. The ANFIS models exhibited overfitting deficiencies that affected learning and training. These findings suggest that the ANN models are a more suitable option for accurate data forecasting in similar engineering applications.