The simultaneous liquid–liquid flow usually manifests various flow configurations due to a diverse range of fluid properties, flow-controlling processes, and equipment. This study investigates the performance of machine learning (ML) algorithms to classify nine oil–water flow patterns (FPs) in the horizontal pipe using liquid and pipe geometric properties. The MLs include Support Vector Machine, Ensemble learning, Random Forest, Multilayer Perceptron Neural Network, k-Nearest Neighbor, and weighted Majority Voting (wMV). Eleven hundred experimental data points for nine FPs are extracted from the literature. The data are balanced using the synthetic minority over-sampling technique during the MLs training phase. The MLs’ performance is evaluated using accuracy, sensitivity, specificity, precision, F1-score, and Matthews Correlation Coefficient. The results show that the wMV can achieve 93.03% accuracy for the oil–water FPs. Seven out of nine FPs are classified with more than 93% accuracies. A Friedman’s test and Wilcoxon Sign-Rank post hoc analysis with Bonferroni correction show that the FPs accuracy using wMV is significantly higher than using the MLs individually (p < 0.05). This study demonstrated the capability of MLs in automatically classifying the oil–water FPs using only the fluids’ and pipe’s properties and is crucial for designing an efficient production system in the petroleum industry.