  Bayesian Machine Learning~(BML) and strong lensing time delay~(SLTD)
techniques are used in order to tackle the $H_{0}$ tension in $f(T)$ gravity.
The power of BML relies on employing a model-based generative process which
already plays an important role in different domains of cosmology and
astrophysics, being the present work a further proof of this. Three viable
$f(T)$ models are considered: a power law, an exponential, and a squared
exponential model. The learned constraints and respective results indicate that
the exponential model, $f(T)=\alpha T_{0}\left(1-e^{-p T / T_{0}}\right)$, has
the capability to solve the $H_{0}$ tension quite efficiently. The forecasting
power and robustness of the method are shown by considering different redshift
ranges and parameters for the lenses and sources involved. The lesson learned
is that these values can strongly affect our understanding of the $H_{0}$
tension, as it does happen in the case of the model considered. The resulting
constraints of the learning method are eventually validated by using the
observational Hubble data(OHD).

Cosmology and Nongalactic Astrophysics;General Relativity and Quantum Cosmology