The state-of-the-art tools for the evaluation of the macroscopic hydrodynamics of cocurrent upflow three-phase fluidization are critically evaluated by thoroughly interrogating the broadest fluidization database ever built. The database is compiled through worldwide conjoint initiatives as a result of a decade of compilation efforts by the groups of professors L. S. Fan (Columbus, OH), S. D. Kim (Seoul, South Korea), and G. Wild (Nancy, France) and our Laval University group. The database represents almost the whole heritage of the nonproprietary data released in the open literature in the field of gas-liquid-solid fluidization (23 000 experiments on bed porosity and liquid, gas, and solid holdups). It is dedicated to embracing wide-ranging fluids' properties, particle and vessel sizes, and operating conditions. The database contains 55 Newtonian (20 500 data), 19 non-Newtonian liquids (2500 data), 110 various particles, and 17 different column diameters and includes wall effect ratios D-c/d(p) and grain sizes ranging from 8 to 800 mm and from 0.25 to 15 mm. Two novel approaches in the field of three-phase fluidization modeling are proposed to reconcile the formidable diversity of patterns and the wide variability of hydrodynamic parameters encountered in this advanced database. Both of them exhibit a substantial gain in their forecasting ability with respect to the currently known prediction methods. The first approach relies on the combination of multilayer perceptron artificial neural networks and dimensional analysis (ANN-DA approach) to derive three highly accurate correlations for bed porosity and liquid and gas holdups. The second is based on a phenomenological hybrid k-x generalized bubble wake model (k-x GBWM) in which the wake parameters it and x are beforehand extracted by solving an inverse k-x GBW model. The ANN-DA approach is then applied to correlate k and x in terms of the accessible fluidization input characteristics and fed into the k-x GBWM to forecast the phase holdups. The robustness of the proposed ANN-DA correlations and k-x GBWM is assessed, and the limitations of the correlations with regard to their generalization capabilities are discussed.