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Constraints on a dark energy dominated Universe are obtained from aninterplay between Bayesian Machine Learning and string Swampland criteria. Theapproach here differs from previous studies, since in the generative processSwampland criteria are used and, only later, the results of the fit arevalidated, by using observational data-sets. A generative process basedBayesian Learning approach is applied to two models and the results arevalidated by means of available $H(z)$ data. For the first model, aparametrization of the Hubble constant is considered and, for the second, aparametrization of the deceleration parameter. This study is motivated by arecent work, where constraints on string Swampland criteria have been obtainedfrom a Gaussian Process and $H(z)$ data. However, the results obtained here arefully independent of the observational data and allow to estimate how thehigh-redshift behavior of the Universe will affect the low-redshift one.Moreover, both parameterizations in the generative process, for the Hubble andfor the deceleration parameters, are independent of the dark energy model. Theoutcome, both data- and dark energy model-independent, may highlight, in thefuture, the borders of the Swampland for the low-redshift Universe and help todevelop new string-theory motivated dark-energy models. The string Swamplandcriteria considered might be in tension with recent observations indicatingthat phantom dark energy cannot be in the Swampland. Finally, a spontaneoussign switch in the dark energy equation of state parameter is observed when thefield traverses are in the $z\in[0,5]$ redshift range, a remarkable phenomenonrequiring further analysis.

Interplay between Swampland and Bayesian Machine Learning in constraining cosmological models