jabbrv-ltwa-all.ldf jabbrv-ltwa-en.ldf Improving cloud cover prediction is one of the fundamental challenges for large-scale climate simulations due to the stochastic nature of clouds and their complex interactions with the atmospheric circulation. This study explores various machine-learning (ML) approaches and utilizes internal and external methods to incorporate Lagrangian air mass history to improve prediction accuracy. Here, we use 169,824 isobaric boundary layer trajectories over the eastern subtropical oceans with colocated meteorological data at 12-hour intervals over 4 days. Satellite cloud cover data from MODIS are colocated with the trajectory points where available, resulting in 43,830 trajectories (26\%) that are fully filled. These are used to train models via a variation of Stochastic Gradient Descent (SGD) called Adaptive Moment Estimation (Adam). All models received 7 cloud-controlling factors (CCF) at each timestamp to predict total cloud cover simultaneously. Several statistical models applied here are found to predict cloud cover with similar or better performance than the leading meteorological reanalysis. The best model using recurrent neural network and cloud cover feedback achieves a correlation coefficient of 0.72 between predictions and MODIS measurements, compared to 0.65 for the reanalysis.   Applications of these models are investigated. We determine sensitivities of cloud cover to cloud-controlling parameters by adding different perturbations to CCFs and recording consequent changes. This sensitivity study reveals a peculiar nonlinear relationship between cloud cover and numerous CCFs. By providing different data-driven perspectives, we begin to leverage the power and speed of ML models as an alternative to the challenge of cloud cover prediction in physical models.