Image-based deep learning (IBDL) is an advanced technique for predicting the surface irradiation conditions of laser surface processing technology. In pulsed-laser surface processing techniques, the number of superimposed laser shots is one of the fundamental and essential parameters that should be optimized for each material. Our primary research aims to build an adequate dataset using laser-irradiated surface images and to successfully predict the number of superimposed shots using the pre-trained deep convolutional neural network (CNN) models. First, the laser shot experiments were performed on copper targets using a nanosecond YAG laser with a wavelength of 532 nm. Then, the training data were obtained with the different superimposed shots of 1 to 1024 in powers of 2. After that, we used several pre-trained deep CNN models to predict the number of superimposed laser shots. Based on the dataset with 1936 images, VGG16 shows a high validation accuracy, higher sensitivity, and more than 99% precision than other deep CNN models. Utilizing the VGG16 model with high sensitivity could positively impact the industries' time, efficiency, and overall production.