Modeling and simulation are indispensable in researching on complex engineering systems. This study attempts to improve the modeling of dynamic response of suspension arm by applying intelligent techniques. The structural model of the suspension arm utilizes the solid works and aluminum alloys (AA7079-T6) as a suspension arm material. The finite element analysis and Radial Basis Function Neural Network (RBFNN) technique are used to predict the response of suspension arm. By utilizing the finite element analysis code, the dynamic analysis is performed. And in the neural network model, there are three inputs represent the load, material and natural frequency, with three outputs representing the Max, Dynamic-displacement T1, T2, T3. Finally, the regression analysis is performed between finite element results and the values predicted by the neural network model. The simulation outcomes show that the proposed RBFNN approach seems highly effective with least error in identification of dynamic-displacement of suspension arm. Also the RBFNN can be very successively used for reducing the effort as well as the time required to predict the dynamic-displacement response of suspension arm, compared with FE methods which usually deals with only one single problem for each run.