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Abstract Stellar parameters for large samples of stars play a crucial role in constraining the nature of stars and stellar populations in the Galaxy. An increasing number of medium-band photometric surveys are presently used in estimating stellar parameters. In this study, we present a machine learning approach to derive estimates of stellar parameters, including [Fe/H], logg, andT_eff, based on a combination of medium-band and broadband photometric observations. Our analysis employs data primarily sourced from the Stellar Abundances and Galactic Evolution Survey (SAGES), which aims to observe much of the Northern Hemisphere. We combine theuv-band data from SAGES DR1 with photometric and astrometric data from Gaia EDR3, and apply the random forest method to estimate stellar parameters for approximately 21 million stars. We are able to obtain precisions of 0.09 dex for [Fe/H], 0.12 dex for logg, and 70 K forT_eff. Furthermore, by incorporating Two Micron All Sky Survey and Wide-field Infrared Survey Explorer infrared photometric and Galaxy Evolution Explorer ultraviolet data, we are able to achieve even higher precision estimates for over 2.2 million stars. These results are applicable to both giant and dwarf stars. Building upon this mapping, we construct a foundational data set for research on metal-poor stars, the structure of the Milky Way, and beyond. With the forthcoming release of additional bands from SAGES such DDO51 and Hα, this versatile machine learning approach is poised to play an important role in upcoming surveys featuring expanded filter sets.