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More than 5000 extrasolar planets have already been detected. JWST and near-term ground-based telescopes like the Extremely Large Telescope (ELT), Giant Magellan Telescope (GMT), Thirty Meter Telescope (TMT), and upcoming telescopes such as the Nancy Grace Roman Space Telescope, Xuntian, and Ariel are designed to characterize the atmosphere of directly imaged Jovian planets. Here, we used five diverse machine learning algorithms to investigate how well broad-band filter photometric fluxes could initially characterize giant exoplanets. We use an established grid of 8813 reflected light model spectra of different metallicities, planet–star distances, and cloud properties to assess the performance of several machine learning algorithms on both noiseless and noisy data to provide classification and regression results as a function of signal to noise of the data. In all cases, the algorithms were tested on noisy validation data. The results show that the use of machine learning to characterize giant planets from reflected broad-band filter photometry provides a promising tool for initial characterization, with over 65 per cent accuracy in characterizing metallicity for signal-to-noise ratios (S/N) ≳ 30, over 80 per cent for cloud coverage for S/N ≳ 30. This approach will allow initial characterization for large surveys of giant exoplanets and prioritization for spectroscopy observations of a subset of these worlds.