Extragalactic transients such as supernovae are some of the most powerful phenomena in the universe, and have enabled multiple studies in thefields such as star formation and stellar evolution beyond the Milky Way,nucleosynthesis and cosmology. Specifics on their progenitors are still anopen question, and host galaxies can provide important context, since theirstellar populations are linked to potential progenitors.Theadvent of unbiased wide-field surveys has provided homogeneous samples of transients, with machine learning codes helping to leverage as muchinformation as possible. In this pursuit, we assembled a sample of transients from the Automatic Learning for the Rapid Classification of EventsLight Curve Classifier discovered by the Zwicky Transient Facility between2018 and 2023, including 22627 transients out to z∼0.5, with 7269 having spectroscopic classifications in the Transient Name Server. Transientswere associated to their host galaxies, for which we extracted photometry,collected redshifts, and fit them with spectral energy distributions to derive physical properties such as stellar mass and star formation rate. Thesefeatures, along some regarding their light curves shapes and peaks, werecompiled into a catalog for publication, one of the largest of its kind.The catalog was used to assess transient properties and transient-hostrelations from the literature. We found consistency with previous worksregarding transient luminosities and physical properties of their host galaxies, with higher statistical significance given the larger sample size. Theseresults set important precedents for the future (e.g., Rubin Observatory).