The increasing cost of fossil fuel and greenhouse gas emissions from the burning of these fuels needs urgent attention. Waste oil from the frying process can be a viable option. It can be converted to biodiesel through the transesterification process. However, optimization can help in the improvement of this process for maximum yield of biodiesel. The limitation of traditional optimization approaches has fueled the hunt for stochastic tools able to successfully explore complicated search spaces while maintaining consistent convergence rates. The gray wolf optimizer (GWO) stands out as a potential algorithm owing to its simplicity and effectiveness in optimization problems. In this work, the potential of GWO was utilized for estimating biodiesel output. There were four control factors, namely molar to oil ratio, catalyst concentration, time, and temperature. Firstly, the analysis of variance was applied to experimental data, and a second-order objective function was developed. It was then used for gray wolf-based optimization. The optimized values for the given parameters were that methanol-to-oil mole ratio (MOMR) was 7.67, temperature (T) was 51.14 °C, catalyst-to-oil concentration (COC) was 0.76 wt./wt. %, reaction time was 71.5 min, and the biodiesel yield was 96.03%. Thus, the GWO model was able to demonstrate its reliability and accuracy in predicting biodiesel production.