Cutter choice becomes a critical issue affecting tool wear in the challenging field of precision manufacturing, which significantly affects output and product quality. To address this important issue, the current work offers an alternative Daubechies wavelet-based cutter selection technique that makes use of the ResNet Deep Learning Model to effectively monitor tool wear. This investigation is based on a systematic examination of vibrational signal patterns that are transformed into scalograms and decoded using the widely recognized ResNet architecture. With an extraordinary accuracy of 96.8%, the experimental results—which are supported by a rigorous five-fold cross-validation methodology—confirm the superiority of ResNet over other models, including EfficientNet and MobileNet, which show accuracies of 95.5% and 94%, respectively. While EfficientNet falls short of ResNet's accuracy peak, it is credited for striking the best balance between computational efficiency and scalability, which positions it as a valuable substitute in contexts with limited computing power. With its optimized architecture for fast and small apps, MobileNet excels in scenarios with limited computing and deployment resources.