The quality of optical components such as lenses or mirrors can be described by shape errors and surface roughness. With increasing optic sizes, the stability of the polishing process becomes more and more important. If not empirically known, the optical surface must be measured after each polishing step. One approach is to mount sensors on the polishing head in order to measure process relevant quantities. On the basis of these data, Machine Learning algorithms can be applied for surface value prediction. The aim of this work is the stepwise development of an artificial neural network (ANN) in order to improve the accuracy of the models' prediction. The ANN is developed in the Python programming language using the Keras deep learning library. Beginning with simple network architecture and common training parameters. The model will then be optimized step-by-step through the implementation of different methods and Hyperparameter optimization (HPO). Data, which is generated by the sensor-integrated glass polishing head, is used to train the ANN-model. A representative part of these data is held back before, in order to validate the models' prediction. The so-called dataset contains measured values from multiple polishing runs, preceded by a design of experiment. After the model is trained on the dataset, it is able to predict the result of not yet performed polishing runs, with given polishing parameters. Concrete, the ANN is used to predict the resulting glass-surface quality, which includes the surface roughness and the shape accuracy, calculated by the material removal over time. The prediction by artificial neural networks reduces the polishing iterations and thus the production time.