The magnitude of wear should be at a minimum for numerous automobile and aeronautical components. In the current work, composites were prepared by varying the cenosphere content using the conventional stir casting method. A uniform distribution of particles was ensured with the help of scanning electron microscopy (SEM). Three major parameters were chosen from various factors that affect the wear. A wear test was conducted with a pin-on-disc apparatus; the controlling parameters were volume percentages of reinforcement of 5, 10, 15, and 20%, applied loads of 9.8, 29.42, and 49.03 N, and sliding speeds of 1.26, 2.51, and 3.77 m/s. The design of the experiments (DOE) was performed by varying the different influencing parameters using the full factorial method. An analysis of variance (ANOVA) was used to analyze the effects of the parameters on the wear rate. Using regression analysis, a response curve was obtained based on the experimental results. The parameters in the resulting curve were optimized using the Genetic Algorithm (GA). The GA results were compared with those of an alternate efficient algorithm called Neural Networks (NNs).