Deep learning has transformed computer vision in the sense that it allows the machine to comprehend and read visual information on human-like precision. Image recognition systems have developed simple pattern detection systems to sophisticated systems of interpretation of scenes and situation analysis, as through advanced neural network designs including convolutional neural networks (CNNs) and generative adversarial networks (GANs). This change has contributed to massive advancements in various industries such as healthcare, manufacturing, transportation, and security, in which image recognition supported by AI is becoming increasingly more automated, aiding decisions and highly accurate in the accuracy of operations. The current developments in the multimodal learning, optimization of models, and unsupervised training have expanded the power of visual recognition systems by enhancing flexibility and scalability to wider applications. Although these have been achieved, there are still challenges including computational efficiency, bias in the data and transparency, all of which indicate that research on interpretable and energy-efficient deep learning models should continue. Finally, deep learning in computer vision is a technological paradigm shift in that it redefines the use of visual intelligence in industries to achieve more efficiency, accuracy, and innovation.