The integration of oxyhydrogen (HHO) gas into internal combustion (IC) engines has attracted substantial interest among researchers in improving engine performance and reducing emissions. In the present work, a wet-type flat-plate electrolyser utilizing sodium hydroxide (NaOH) as electrolyte is investigated to determine the interdependent effects of voltage, current, and NaOH concentration on HHO gas generation rate and system efficiency. The results show that moderate current and voltage levels, along with higher NaOH concentrations (e.g., 5.87 V and 1 N) yield a maximum gas production rate of 0.5 L/min while conserving energy efficiency. The experimental analysis also showed that as the current increase the rate of production also increased. The maximum production of 0.5 L/min was achieved with 30 A. The study also extends to use experimental data to train machine learning algorithm to estimate the performance of the HHO gas system. Voltage, current, power consumption, resistance and electrolyte concentration were used as input parameters while efficiency and HHO gas production were the output parameters measured with a total dataset size of 112 observations. To reduce the experimental burden and establish an efficient predictive framework five gradient boosting algorithms namely, categorical boosting (CatBoost), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), adaptive boosting (AdaBoost) and gradient boosting (GB) are evaluated among which CatBoost achieved maximum accuracy with R2 values of 0.9903 (for hydrogen production) and 0.9583 (for efficiency) on test data. The findings highlight how crucial intermediate operating conditions are for optimizing gas output and efficiency while lowering resource usage.