Sea surface temperature (SST) is a vital component of the climate system, and its spatiotemporal variations significantly influence global climate and ecological equilibrium. Unlike most existing univariate SST prediction models that neglect atmospheric forcing information, this study proposes a machine learning-driven multivariate framework integrating key meteorological variables to learn data-driven, nonlinear relationships...