Drilling rate of penetration is one of the most important parameters in optimization and cost reduction of drilling operation. In order to predict the rate of penetration with high precision, artificial intelligence methods are used in this paper. Drilling data of reservoir zone in one of the Maroun oilfield wells consisting of drilling operation parameters, drilling fluids properties, and rock mechanics properties, extracted from mud logging and petrophysics logs, is collected. 8 input parameters such as weight on bit, bit rpm, pump pressure, fluid specific gravity, fluid plastic viscosity, fluid yield point, shale volume, porosity, unconfined compressive strength, Young modulus and toughness are chosen as the most effective parameters on rate of penetration. Using these parameters, two artificial neural network models MLP-PSO and MLP-BP were developed to predict rate of penetration and their performances are evaluated. This study shows that artificial neural network trained by particle swarm optimization have better performance than other models in predicting rate of penetration. The performance of this model is compared with empirical models and the results show the superior performance of artificial neural network models over these models in predicting rate of penetration. Besides requiring more calculation time, these empirical models show lower precision in predicting drilling rate of penetration.