A confined plunging liquid jet reactor (CPLJR) is an unconventional efficient and feasible aerator, mixer and brine dispenser that operates under many operating conditions. Such operating conditions could be challenging, and hence, utilizing prediction models built on machine learning (ML) approaches could be very helpful in giving reliable tools to manage highly non-linear problems related to experimental hydrodynamics such as CPLJRs. CPLJRs are vital in protecting the environment through preserving and sustaining the quality of water resources. In the current study, the effects of the main parameters on the air entrainment rate, Q_a, were investigated experimentally in a confined plunging liquid jet reactor (CPLJR). Various downcomer diameters (D_c), jet lengths (L_j), liquid volumetric flow rates (Qj), nozzle diameters (d_n), and jet velocities (V_j) were used to measure the air entrainment rate, Q_a. The non-linear relationship between the air entrainment ratio and confined plunging jet reactor parameters suggests that applying unconventional regression algorithms to predict the air entrainment ratio is appropriate. In addition to the experimental work, machine learning (ML) algorithms were applied to the confined plunging jet reactor parameters to determine the parameter that predicts Q_a the best. The results obtained from ML showed that K-Nearest Neighbour (KNN) gave the best prediction abilities, the proportion of variance in the Q_a that can be explained by the CPLJR parameter was 90%, the root mean square error (RMSE) = 0.069, and the mean absolute error (MAE) = 0.052. Sensitivity analysis was applied to determine the most effective predictor in predicting Q_a. The Qj and V_j were the most influential among all the input variables. The sensitivity analysis shows that the lasso algorithm can create an effective air entrainment rate model with just two of the most crucial variables, Qj and V_j. The coefficient of determination (R²) was 82%. The present findings support using machine learning algorithms to accurately forecast the CPLJR system’s experimental results.

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