Global industrialisation and population growth have fuelled a significant rise in the demand for bioethanol, a sustainable and eco-friendly biofuel. Bioethanol production supports circular economy and zero-waste goals. Utilising ethanol as an alternative fuel reduces reliance on petrochemicals while bolstering energy security and global environmental sustainability. Commercial bioethanol production has traditionally relied on food-based agricultural crops such as sugarcane and sugar beet, often considered as resources primarily intended for human and animal consumption. Second-generation (2G) ethanol production, which uses lignocellulosic or agro-industrial biomass, involves a detailed three-step process: pretreatment, saccharification, and fermentation. Enzymatic saccharification is particularly critical for efficiently extracting fermentable sugars from biomass. The saccharification of biomass is significantly impacted by factors such as pretreatment methods, enzyme dosage, pH, temperature, substrate and surfactant concentrations, and reaction time. Optimising these variables is vital for maximising bioethanol production and minimising energy consumption. Design of Experiment (DoE) tools, combined with response surface methodology (RSM), offer an effective strategy for optimisation, providing a systematic means to understand the effect of several factors on saccharification and enabling efficient process optimisation. DoE and RSM facilitate achieving optimisation goals with fewer experiments, reduced time, and lower costs. This chapter summarises the successful application of DoE and RSM tools for optimising biomass saccharification. It also provides an overview of the primary experimental design tools including the application of artificial intelligence (AI) and machine learning (ML), highlighting their significance in improving bioethanol production efficiency.