Wellhead choke is an integral part of production systems. Wellhead chokes act as regulators to protect surface facilities from high rate slugs, formation damage, and water coning. These chokes also control the decline rate of reservoir pressure. Accordingly, a diverse data bank of critical flow across wellhead chokes has been compiled to develop a universal choke performance model. Three models were developed to predict liquid flow rate in twophase flow through chokes. The first two models are measured by computational intelligence paradigms such as the Artificial Neural Network and the Least Square Support Vector Machine. The third model was developed using a global optimization simplex algorithm. The three models outperformed all existing models, and the results clearly highlight the accuracy and superiority of the intelligence models over the existing empirical correlations.