New robust correlations and mechanistic model of macroscopic fluid dynamic and gas-liquid mass transfer characteristics for randomly packed towers were developed based on first principles, neural network computing and dimensional analysis (artificial neural net-work and dimensional analysis, ANN-DA). These tools concerned the loading and flooding capacities, the total liquid hold-up, the irrigated pressure drop, the local volumetric liquid-side, k(L)a, and gas-side, k(G)a, mass transfer coefficients, the overall volumetric, K(L)a and K(G)a, mass transfer coefficients, and the packing fractional wetted area. Validation of these tools was performed by interrogating a broad experimental database including over 10,750 measurements published in the literature over the past seven decades. The fully-predictive mechanistic model proved powerful in forecasting the tower hydraulics below the loading point without requiring any adjustable parameter. On the other hand, the ANN-DA correlations proved highly powerful in correlating the tower fluid dynamics and gas-liquid mass transfer regardless of the operating flow regime. These approaches were also benchmarked with respect to the comprehensive Billet and Schultes (Trans. Industr. Chem. Eng. 77 (1999) 498) phenomenological approach and the classical Onda et al. (J. Chem. Eng. Japan 1 ( 1968) 56) mass transfer correlations.