Abstract In this work, optimization of oil extraction from the Citrullus lanatus (C. lanatus) seed was carried out. To determine the qualities of the oil, physiochemical properties of the oil was also carried out. This was with a view to add value to C. lanatus oil and finding environmentally friendly alternative to conventional oil. Optimization of oil extraction from the seed was carried out using a three-level-three-factors response surface methodology (RSM) and artificial neural network (ANN). Seventeen (17) experimental runs were generated and were carried out. Result showed the highest CLOY of 35.65 (% w/w) was obtained at a coded factors of X1 = -1, X2 = -1 and X3 = 0, but the statistical RSM software predicted CLOY of 28.1383 (% w/w) at X1 = -1, X2 = -0.621 and X3 = -1 variable conditions, and this was validated by carrying out three experiments, and an average CLOY of 28.10 (% w/w) was obtained. Similarly, statistical ANN software predicted CLOY of 32.301 (% w/w) at X1 = – 0.78, X2 = -1 and X3 = 0.70 variable conditions, which was validated by carried out three experiments, and the average contents of CLOY was 31.80 (% w/w). The coefficient of determination (R2) and R-Sq. (adj.) were found to be 99.98% and 99.96% (RSM), 99.993% and 99.986% (ANN), respectively. The qualities of oil extracted from the C. lanatus seed under optimal condition showed that the oil is non-edible and could serve as feedstock for many industrial applications. Fatty acid composition of the oil showed that the oil is highly unsaturated (79.82%). The finding concluded that C. sinensis seed is reached in oil and RSM proved suitable in experiment and statistical analysis, but ANN predicted better than the RSM in terms of C. lanatus oil yield (CLOY).