Lithology identification is an important aspect in reservoir characterization with one of its main purpose of well planning and drilling activities. A faster and more effective lithology identification could be obtained from an ensemble of optimized models using voting classifiers. In this study, a voting classifier machine learning model was developed to predict the lithology of different lithologies using an assembly of different classification algorithms: Support Vector Machine (SVM), Logistic Regression, Random Forest Classifier, K-Nearest Neighbor, and Multilayer Perceptron (MLP) models. The result of the comparative analysis shows that the implementation of the voting classifier model helped to increase the prediction performance by 1.50% compared to the individual models. Despite a small significance at deployment in real scenario it improves the chances of classifying the lithology.