Three-phase induction motors (TPIMs) are compact and robust electrical machines, which typically require the determination of equivalent electrical circuit parameters. In this context, this paper aims to obtain the parameters of a single-phase equivalent circuit for the TPIM, based on the double cage model, using Artificial Neural Networks (ANNs) and Decision Tree-Based Algorithms (DTAs), trained with a dataset composed of 860 motors spanning a power range of up to 370 kW. Afterward, comparative analyses are conducted to evaluate the effectiveness of using each of the different approaches to obtain parameters of the double-cage model compared to the parameters obtained using the Modified Newton Method, which is integrated into widely used engineering tools such as MATLAB/Simulink. The obtained results suggest that the DTAs demonstrated superior performance in predicting the internal parameters, given the dataset's size and characteristics. This outcome emphasizes their applicability in real-world scenarios.