In the era of artificial intelligence, the proliferation of AI-generated images has blurred the boundaries between reality and digital fabrication. Technologies such as Generative Adversarial Networks (GANs) have enabled the creation of highly realistic synthetic images—commonly known as deepfakes—which pose substantial challenges in domains like digital media, cybersecurity, and legal forensics. While these advancements offer innovative applications in entertainment and simulation, their potential misuse can lead to misinformation, identity theft, and erosion of public trust. This project proposes an AI-powered image authenticity detection system that leverages a Convolutional Neural Network (CNN) to accurately classify images as either real or AI-generated. The system is built with an intuitive graphical user interface (GUI) that allows users to upload and analyse images in both individual and batch modes. Key features include real-time prediction with confidence scoring, visual result displays, confusion matrix generation, and performance metrics such as accuracy, precision, and recall. The model achieves an overall classification accuracy of 82.7%, demonstrating strong potential for real-world applications in detecting synthetic media. By combining deep learning techniques with user-centric design, the system provides a practical and transparent solution for addressing the rising concerns of digital image manipulation. It serves as a critical tool for enhancing media authenticity and combating the spread of AI-generated misinformation.