Artificially generated images have rapidly risen in both quality and quantity, largely driven by their popularity across domains due to their ease of access and use. This poses threats of misinformation and lack of trust in artificially generated images that pass as real, particularly in news and social media. Current research focuses on the development of methods to enhance detection, improving generalization, or on the addition of interpretability factors using explainable artificial intelligence (XAI). This project addresses the need for a detection system that allows for efficient prediction of “AI-generated” images across a variety of image generators and of various content types over “Real” images, whilst providing insights and explanations for the prediction. In this research, a novel approach is proposed for the detection of AI-generated images while integrating Explainable Artificial Intelligence techniques to facilitate interpretability. The proposed solution implementation was developed using a Convolutional Neural Network (CNN) classifier built on top of the DenseNet121 architecture, which leveraged pre-trained weights from ImageNet, allowing for efficient feature extraction. The model was trained on a curated dataset consisting of images from five AI-image generators, which included Midjourney, Stable diffusion, Dall-E, ProGAN, and other images from State-of-The-Art image generators. The proposed solution was evaluated using a range of data science metrics, including the F1 score, precision, accuracy and recall on an unseen portion of the dataset. The initial results prove promising, achieving on average an accuracy of 93% while maintaining an F1-score of 93% and loss of 0.22.