Artificial neural network (ANN) approach, analysis of variance (ANOVA) and multiple regression model were developed to predict the wear rate of Aluminum (Al)-Silicon (Si) alloy. These methods based on weight fractions of Alumina (Al2O3), load and sliding distance as inputs. Metal matrix composites (MMCs) prepared using stir casting method. The Al-Si alloy was reinforced with the addition 0, 10, 15, 20, and 25 wt% Al2O3 particles. The ANN model was obtained to predict the wear rates of the composites. Experimental results indicated that the increase of both load and sliding distance increases the wear rate. However, the increase of weight fractions of Alumina (Al2O3) decreases the wear rate. Both ANN and ANOVA revealed that the sliding distance has the major influence on the wear rate followed by alumina weight fraction, however the applied load has a relatively low influence on the wear rate of Al-Si/ Al2O3 composite. A multiple regression approach suggested in this study correlate the weight fraction of Al2O3, load and sliding distance with wear rate.