This study compares the use of convolutional neural networks (CNNs) to improve the photo identification accuracy. The impact of several CNN designs, training strategies, and optimization techniques on the ImageNet and CIFAR-10 benchmark datasets was investigated. While outlining the benefits and drawbacks of each tactic, the report provides fact-based recommendations. Our findings aim to support researchers and practitioners in deploying reliable picture-recognition algorithms in real-world applications. Our analysis highlights key variables, provides helpful recommendations for improving the image identification accuracy, and highlights the benefits and drawbacks of different models. Through this work, computer vision capabilities ware improved, leading to new and more reliable applications in a variety of industries.