Abstract Evaluating the elastic parameters of reservoir section are very essential for oil and gas industry in alleviating the risks associated with the drilling and production phases of the reservoir. These parameters are used in the optimization of the well placement, well bore instability, completion design, and draw-down limits to avoid sanding, hydraulic fracturing, reservoir subsidence and in many more applications. To carry out any aforementioned operations, a continuous profiles of rock elastic parameters are needed. Misleading estimation of elastic parameters may wrongly lead to heavy investment decisions and inappropriate field development plans. Retrieving reservoir rock samples throughout the depth of the reservoir section and performing laboratory tests on them are extremely expensive as well as time consuming. Therefore, these parameters are estimated from empirical correlations. Most of the previous models/correlations were developed using linear or non-linear regression techniques. Artificial intelligence tool once optimized for training can successfully model elastic parameters since these tools are capable of capturing highly complex and non-linear relationship between the input parameters and the target parameter. The objective of this study is to develop a robust and an accurate model for static Young's modulus based on the wireline logs as an input using three artificial intelligence tools (artificial neural network, adaptive neuro fuzzy inference system and support vector machine). The data on which these AI models are built comprises of more than 680 real field data points from different fields covering a wide range of values. Based on the minimum error and the highest coefficient of determination between actual and simulated data artificial neural network is selected as the proposed AI model to predict static Young's modulus. A comparison between the static Young's moduli predicted by the proposed model with the published models /correlations reveals that neural network model gives significantly less average absolute percentage error. Finally, a rigorous empirical correlation is developed using the weights of ANN model in order to make the AI black box model as a white box and universal that can be usable for field applications.