Modern datasets for machine learning and AI applications leverage vast data across multiple domains and often exhibit temporal dependence. Several challenges arise with analyzing these large-scale datasets: (1) missing data due to complex mechanisms, (2) classification under class imbalance due to biased data collection, and (3) network analysis for learning and quantifying temporal interactions in multivariate time series. In particular, Generative Adversarial Networks (GANs) and its variants offer a promising solution for the first two data quality challenges. Bayesian Spillover Graphs (BSG), which utilize classic time series forecasting and impulse response analysis with Bayesian adaptions, provide a new perspective via graphical representations and interpretable network measures for understanding dynamic systems. Bayesian Spillover Graph is a novel method for learning temporal relationships, identifying critical nodes, and quantifying uncertainty for multi-horizon spillover effects within a dynamic network. BSG leverages both an interpretable framework via forecast error variance decompositions and comprehensive uncertainty quantification via Bayesian time series models to contextualize temporal relationships in terms of systemic risk and prediction variability. Forecast horizon hyperparameter h allows for learning both short-term and equilibrium state network behaviors. BSG also serves as an exploratory analysis tool for uncovering indirect spillovers and quantifying systemic risk, with potential applications in areas of econometrics, biostatistics, and social and applied statistics. The Imputation Balanced GAN (IB-GAN) is an unique framework that joins data augmentation and classification in a one-step process. IB-GAN uses imputation and resampling techniques to generate higher quality samples from randomly masked data vectors and augments classification through a class-balanced set of real and synthetic data. Imputation hyperparameter p_miss allows for regularization of innovations introduced via generator imputation. IB-GAN pairs any deep learning classifier with a generator-discriminator duo and results in higher accuracy for under-observed classes. The Conditional Imputation GAN is an extended missing data imputation method based on GANs. Empirical datasets for machine learning applications often do not follow standard Gaussian distributions or Missing Completely At Random mechanisms. Our methodology offers compatible imputation guarantees while relaxing assumptions for missing mechanisms. We prove that the optimal GAN imputation is achieved for Extended Missing At Random and Extended Always Missing At Random mechanisms.