Hydrogen Sulphide Methane Reformation (HSMR) is a viable alternative for simultaneous H₂S valorization and hydrogen production, offering a carbon-neutral alternative. Concurrently, the industrial revolution, driven by emerging technologies like machine learning and artificial intelligence is reshaping chemical  industry as well. machine learning offers robust models for accurate process forecasting, overcoming computational demands and inflexibility. This work focuses on developing a surrogate model for the HSMR process, a promising, waste to energy, route for sustainable hydrogen production. Integrating machine learning with design of experiment techniques, the study systematically generates data using Aspen plus v11 simulation and employs established adaptive space filling sampling techniques. Models such as Linear Regression, Support Vector Regression, Random Forest Regression, Extreme Gradient Boosting, and Artificial Neural Networks was trained on the refined data set, an accuracy of up to 97% in predicting the process outputs was achieved.  Furthermore, this research extends to the optimization of an artificial neural network (ANN) surrogate model using evolutionary algorithms, specifically genetic algorithms and differential evolution. within the Python environment, this optimization aims to identify optimal process conditions for HSMR. A comparative analysis with the base case provides insights into the effectiveness of these evolutionary algorithms in enhancing the performance of the ANN model. This work contributes to enhancing the efficiency and competitiveness of HSMR, bridging the gap between machine learning capabilities and sustainable energy solutions.