Computational Fluid Dynamics (CFD) serves as a powerful tool for simulating fluid flow across diverse industries. High-resolution CFD simulations offer valuable insights into fluid behavior and flow patterns. As resolution increases, computational data requirements and time rise proportionately, posing a persistent challenge in CFD. Recent efforts focus on accurately predicting fine-mesh simulations from coarse-mesh data, employing deep learning techniques such as UNets to address this challenge. Existing methods face limitations with unstructured meshes, due to their inability to convolute. Incorporating geometry/mesh information during training brings drawbacks like increased data requirements, and challenges in generalization to unseen geometries. To address these concerns, we propose a novel framework, PointSAGE a mesh-independent network that leverages the unordered, mesh-less nature of Pointcloud to learn the complex fluid flow and directly predict fine simulations, completely neglecting mesh information. With an adaptable framework, PointSAGE accurately predicts fine data across diverse point cloud sizes, regardless of the training dataset's dimension. Evaluations of various datasets and scenarios demonstrate notable results, showcasing a significant acceleration in computational time for generating fine simulations compared to standard CFD.