In the era of smart cities and AIoT infrastructure, deploying efficient machine learning models on resource-constrained edge devices has become critical for urban utility management. This paper evaluates the effectiveness of various data augmentation techniques in enhancing the performance of machine learning models on these devices despite their limited computational resources. Our study utilizes three datasets of digit images: one from Kaggle, one from SCUT, and a proprietary dataset. We tested ranges of parameters for data augmentation, including noise, brightness, contrast, and geometric transformations, to assess their impact on model accuracy. The findings indicate that while augmentation generally improves model performance, an optimal range exists beyond which accuracy may decline due to overfitting. This paper describes this standardized approach to parameter testing that contributes to developing more efficient and accurate edge-based machine learning applications.