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Ejectors have gained renewed interest in the last decades, especially in heat-driven refrigeration systems, to reduce the load of the compressor. Their performance is usually influenced by many factors, including the working fluid, operating conditions and basic geometrical parameters. Determining the relationships between these factors and accurately predicting ejector performance over a wide range of conditions remain challenging. The objective of this study is to develop fast and efficient models for the design and operation of ejectors using artificial neural networks. To this end, two models are built. The first one predicts the entrainment and limiting compression ratio given 12 input parameters, including the operating conditions and geometry. The second model predicts the optimal geometry given the desired performance and operating conditions. An experimental database of ejectors using five working fluids (R134a, R245fa, R141b, and R1234ze(E), R1233zd(E)) has been built for training and validation. The accuracy of the ANN models is assessed in terms of the linear coefficient of correlation (R) and the mean squared error (MSE). The obtained results after training for both cases show a maximum MSE of less than 10% and a regression coefficient (R) of, respectively, 0.99 and 0.96 when tested on new data. The two models have then a good generalization capacity and can be used for design purposes of future refrigeration systems.