  We study the late-time cosmological tensions using the low-redshift
background and redshift-space distortion data by employing a machine learning
(ML) technique. By comparing the generated observables with the standard
cosmological scenario, our findings indicate support for the phantom nature of
dark energy, which ultimately leads to a reduction in the existing tensions.
The model-independent approach also enables us to examine the combined
background and perturbative history, where tensions are reduced. Moreover, from
a statistical perspective, we have shown that our results exhibit a better fit
to the data when compared to the $\Lambda$CDM model.
