  Aims. We explore machine learning techniques to forecast star formation rate,
stellar mass, and metallicity across galaxies with redshifts ranging from 0.01
to 0.3.
  Methods. Leveraging CatBoost and deep learning architectures, we utilize
multiband optical and infrared photometric data from SDSS and AllWISE, trained
on the SDSS MPA-JHU DR8 catalogue.
  Results. Our study demonstrates the potential of machine learning in
accurately predicting galaxy properties solely from photometric data. We
achieve minimised root mean square errors, specifically employing the CatBoost
model. For star formation rate prediction, we attain a value of RMSESFR = 0.336
dex, while for stellar mass prediction, the error is reduced to RMSESM = 0.206
dex. Additionally, our model yields a metallicity prediction of RMSEmetallicity
= 0.097 dex.
  Conclusions. These findings underscore the significance of automated
methodologies in efficiently estimating critical galaxy properties, amid the
exponential growth of multi-wavelength astronomy data. Future research may
focus on refining machine learning models and expanding datasets for even more
accurate predictions.
