Considering established relevance to the GTSRB dataset, it is important to emphasize that research investigates the effectiveness of convolutional neural networks (CNN) in the field of road sign recognition. Following that wide range of techniques for comprehensive preprocessing pipelines were implemented, including data normalization and augmentation as well as resizing images. The CNN model has demonstrated the ability to overcome adverse conditions across multiple road sign classes, demonstrating outstanding scores against the performance metrics used in testing and evaluation process. Model achieved classification accuracies exceeding 99% across most categories. Nevertheless, in certain classes there is presence of performance metric decline related to the inaccurate visualization and contradiction of features. The crucial role of the preprocessing phase has been highlighted while the implementation of the CNN model has been identified as one of the most reliable approaches in the field of road sign recognition. However future implications must be considered to achieve the full potential of the model. Some of the crucial contributions for the future will be introducing real life variation in the dataset. On the other hand, occlusion, lighting and weather conditions are the important factors that should be brought into focus.