In recent years, there has been significant interest in using data-driven methods to solve problems in science and engineering. Numerical simulations for these problems can be costly, making data-driven methods valuable for understanding and improving efficiency in quantifying and predicting states. This talk will review recent advancements in Scientific Machine Learning for Coupled Fluid Flow and Transport, such as dynamic mode decomposition, physics-informed neural networks, manifold learning, and neural operators, as applied to relevant problems. These problems are of interest in sustainable resource exploration, geophysics, and various industrial applications. The talk will show how data-driven information can improve predictions, help explore parametric manifolds for unseen scenarios, and reconstruct high-dimensional simulations with lower-dimensional structures in a feasible time.