Comparing real and synthetic high-quality images for classification tasks in deep learning is the foundation of developing innovative computer vision systems. This paper, therefore, aims to explore the impact of synthetic images after training deep neural networks (DNNs) and compare them with real images. Some of the areas explored include photorealism ranging from high to low, characteristics of the datasets, and the effects it will have on the accuracy and versatility of the models. Methodologies include the synthesis of the images, some factors that define the selection of a dataset, and the architectures of a deep learning model that should be appropriate for classification. Evaluation measures such as accuracy, precision, recall, and F1 measures are used to scientifically analyze model performances. This research investigates the approaches in transfer learning and domain adaptation to address the differences between the synthetic data and the real domain environment. Some of the critical and emergent ethical questions involving the use of synthetic training data are also highlighted alongside the realist discussions on the practicality and deployment of AI models that have been trained on synthetic data. As such, to learn how to apply synthetic images successfully in state-of-the-art deep learning applications, this study seeks to present an elaborate conceptual analysis of such an application through extensive experimentation while pointing at future research directions.