In this review we explore cutting-edge methods for integrating complex multi-omics datasets, a key challenge in precision medicine. We spotlight deep generative models—especially variational autoencoders (VAEs)—for tasks like data imputation, denoising, and joint embedding, and examine how advanced training strategies are transforming multi-modal data analysis. From classical statistics to next-gen deep learning, we chart the path toward more accurate, holistic disease insights.