Computer vision and image processing are the subjects of this study, which investigates geometric and algebraic machine learning. Our ability to extract features and add data is facilitated by the use of vector measure spaces, manifolds, and algebraic structures such as groups and tensors. For the purpose of stabilizing and agreeing models, our technique makes use of orthogonal sets and sequences. Improvements in representations derived from high-dimensional visual input can be achieved by the application of techniques that combine geometric sensibility and mathematical rigour. The results of trials indicate that these tactics are effective in improving computer vision tasks such as object recognition and three-dimensional form analysis.