Hydrogen gas has emerged as a promising and ecologically friendly substitute for fossil fuels, providing a long-term energy alternative. This research study applies machine learning regression models for estimating the volume of HHO gas generated using two distinct electrolytes with varying concentrations, namely sodium hydroxide (NaOH) and potassium hydroxide (KOH). The efficiency of the HHO gas production system hinges on various factors, such as the type of electrolyte and electrode, distance between electrodes, applied current and voltage. This investigation employed a comprehensive array of 29 multiple-regression algorithms to predict gas production. Notably, the input data for these algorithms were directly fed from the collected experimental data without any preprocessing. The input variables include the electrolyte concentration, current, and voltage, while the output is the estimated production of HHO gas. The findings of this study revealed that the multilayer perceptron (MLP) regression algorithm yielded the highest correlation coefficients, registering values of 0.9945 for NaOH and 0.9942 for KOH, respectively. Root relative squared error, relative absolute error, root mean squared error, mean fundamental error, and correlation coefficient were the metrics used to determine the model performance in predicting the volume of HHO gas produced. Furthermore, the experimental analysis demonstrated a direct relationship between the concentration of the electrolyte solution and the current with the rate of HHO gas production. Notably, the study identified that operating the cell at 40 A with KOH resulted in the maximum productivity of 0.9 litres per minute (LPM) of HHO gas, representing a 16 % increase compared to NaOH. These findings underscore the potential of machine learning regression models in optimizing hydrogen production and highlight the advantages of KOH as an electrolyte in this context.