Marine low clouds significantly cool the climate, but predicting these clouds remains challenging: the response of these clouds to various factors is highly non-linear. Previous studies usually overlook the effects of cloud droplet number concentration (Nd) and the non-local information of the target grids. To address these challenges, we introduce a convolutional neural network model (CNNMet-Nd) that uses both local and non-local information and includes Nd as a cloud-controlling factor to enhance the predictive ability of cloud cover, albedo, and cloud radiative effects (CRE) for global marine low clouds. CNNMet-Nd demonstrates superior performance, explaining over 70% of the variance in these three cloud variables for instantaneous scenes of 1°×1°, a notable improvement over past efforts. CNNMet-Nd also accurately replicates geographical patterns of trends in marine low clouds from 2003 to 2022. In contrast, a similar convolutional neural network model without Nd input (CNNMet) fails to predict global mean cloud properties effectively, underscoring the critical role of Nd. Further comparisons with an artificial neural network (ANNMet-Nd) model, which uses the same inputs but without considering spatial dependence, show CNNMet-Nd’s superior performance with R2 values for cloud cover, albedo, and CRE being 0.16, 0.11, and 0.18 higher, respectively. This highlights the importance of incorporating non-local information into low cloud predictions to enhance climate model parameterizations.