The estimation of the seismic bearing capacity of strip footing is of paramount importance in geotechnical engineering. In case of a shallow strip footing above voids in heterogeneous soil, the assessment of its said bearing capacity turns out to display a complex dependency on various parameters, linked to the geometry of the void and the properties of the soil. Recent research activities have highlighted that a methodology based on the combination of sensitivity analysis and machine learning can be extremely efficient in catching such a complex dependency. For the training of the ML technique, a database consisting of 38,000 Finite Element Limit Analysis (FELA) models has been adopted in this work. With the aim of estimating the mentioned seismic bearing capacity, five strategies have been investigated to select the training and test data. By considering the seismic bearing capacity as the single output parameter of the ML-based algorithm, and void depth and eccentricity, soil undrained shear strength and rate of change of its cohesion with the depth, and horizontal seismic acceleration as input parameters, the methodology has provided the most accurate results in mimicking the numerical, FELA-based reference solutions.