This project aims to develop machine learning-based fast inversion techniques for matrices in implicit isogeometric discretizations. Implicit isogeometric discretizations are known for their accuracy and stability in solving complex time-dependent partial differential equations. However, matrix inversions in these methods can be computationally intensive. In this project, we aim to revolutionize the efficiency of matrix inversion by leveraging machine learning algorithms. Through comprehensive training and optimization, our intelligent algorithms will accelerate the inversion process, enabling faster computations in implicit isogeometric discretizations. This innovative endeavor sits at the intersection of machine learning and computational mathematics.