The porosity of the petroleum reservoirs is considered one of the most important parameters in reserve estimation because it determines the effective volume of the hydrocarbon that is stored in the reservoir. Based on the reserve estimation, the development plan can be set and managed. Porosity can be determined in the laboratory which is the most expensive methods. Porosity also can be determined from the logs such as density, neutron, sonic, and NMR logs. There are a lot of uncertainties in the porosity estimation from wireline logs because it depends on many statistical analysis and also is affected by the logging environment and logging tools. The prediction of the porosity from different porosity logs using artificial intelligence (AI) methods validated with the laboratory measured values is the best method to determine an accurate value of the rock porosity. The objective of this research is to evaluate AI tools such as artificial neural network (ANN), Support vector machine (SVM) and Adaptive neuro fuzzy inference system (ANFIS) to predict the reservoir porosity based on wireline log data. More than 1700 field measurements of porosity with logs data were used for training and testing the AI techniques. The results obtained showed that ANN and ANFIS can be used to estimate the reservoir porosity based on log data with a high correlation coefficient (R) and low average absolute percentage error (AAPE). The main inputs required for porosity estimation are bulk density, neutron porosity, and sonic compressional time. The developed mathematical equation based on the weights and bias of the ANN model can be used to predict the reservoir porosity based on log data with a correlation coefficient of 0.98 and an AAPE less than 8%. The advantage of this work is that we extracted the mathematical model from the ANN that can be used directly to determine the porosity without the need for training and testing the data. The porosity estimation from the neutron-density crossplots, which is the current technique used by the industry, yielded 14.7% error.