Abstract—The ability of Generative Adversarial Networks (GANs) to produce images that closely resemble real ones has raised concern. This requires the creation of efficient detection techniques because it has significant ramifications for digital media, security, and ethics. In order to demonstrate the growing difficulties of attaining authenticity in the rapidly developing field of Artificial Intelligence (AI), this study introduces this critical issue by leveraging the “Detect AI-Generated Faces: High-Quality Dataset,” obtained from Kaggle which contains 3,203 images of real human faces and AI-generated faces. However, the Orange3 data mining framework is used to analyze these images, focusing on extracting essential features such as shape attributes, texture descriptors, and color histograms. The dataset was divided into a training set (70%) and a testing set (30%) to evaluate our models effectively. Also, four machine learning algorithms were employed: K-Nearest Neighbor (KNN), Artificial Neural Network (ANN), Adaptive Boosting (AdaBoost), and Gradient Boosting (GB). The results revealed that KNN and AdaBoost achieved impressive accuracies of 99.4% and 97.07%, respectively, while GB and ANN reached even higher accuracies of 99.8% and 99.9%. These results underscore the effectiveness of advanced machine learning techniques in accurately distinguishing between AI-generated and real faces.