In the current research. nano-composites of commercial casting aluminum alloy reinforced with nano-silicon carbide were produced. Experimental and modeling investigations were carried out on the porosity, wear, hardness, elongation, yield strength and ultimate tensile strength (UTS) of these nano-composites. The density measurements showed that the amount of porosity in the composites increased with increasing the volume fraction of nano-particles. The wear sliding test revealed that composites offer superior wear resistance as compared to the alloy irrespective of nano-particles volume fraction. The tensile strength results show that the yield strength and ultimate tensile strength of the composites increased with increasing volume fraction of nano-particles. The incorporation of nano-particles deteriorates the ductility of A356 alloy. However, the elongation remains rather constant with increasing the volume fraction of nano-particles. In the other word, increasing the nano-sized ceramic particles content helps to strengthen the composites, while the ductility is retained. In this paper, a technique based on artificial neural network (ANN) and finite element method (FEM) was proposed to investigate on nano-silicon carbide reinforced commercial casting aluminum alloy matrix. It was observed that prediction of this study is consistent with experimental measurements for aluminum matrix composites. (C) 2011 Elsevier B.V. All rights reserved.