The Soil wetting pattern is a key factor in the optimal design and management of surface and subsurface drip irrigation systems, and its accurate characterization plays a decisive role in determining appropriate emitter and lateral spacing. In this study, the performance of several widely used wetting bulb estimation models, including Schwartzman, Amin and Ekhmaj, Karimi, Kandelous, and Al-Ogaidi, was evaluated and compared using laboratory experimental data. Experiments were conducted in a transparent rectangular physical model with dimensions of 3 m × 0.5 m × 1 m, using three soil textures (light, medium, and heavy). Emitters were installed at four depths (soil surface, 15, 30, and 45 cm) and operated at three discharge rates of 2.4, 4, and 6 L h⁻¹ over irrigation durations of 2 and 6 hours. Comparison between measured and simulated values indicated that the Al-Ogaidi model provided higher accuracy in predicting both horizontal and vertical wetting front distributions compared with the other models. The mean RMSE values for horizontal wetting front distribution in surface drip irrigation at a discharge of 4 L h⁻¹ were 0.071, 0.032, and 0.034 for clay, loam, and sandy soils, respectively, while the corresponding values for vertical distribution were 0.017, 0.019, and 0.072. The superior performance of this model can be attributed to its simultaneous consideration of a comprehensive set of soil physical and hydraulic parameters, enabling a more realistic representation of the complex processes governing water movement and distribution in porous media. Overall, the results demonstrate that multi-parameter empirical models can serve as efficient tools for improving design accuracy, enhancing management practices, and increasing the overall efficiency of surface and subsurface drip irrigation systems.