In today’s digital landscape, the proliferation of social media and powerful tools has made creating deepfake content remarkably easy. These manipulated images and videos can spread false information, lies, and misleading messages, leading to widespread fear and confusion. To address this, a robust system capable of distinguishing between authentic and fabricated images is essential. Our study introduces an automated approach for categorizing deepfake images using deep learning techniques. Unlike traditional machine learning systems, which rely on manual feature extraction and often struggle to capture complex patterns and adapt to new data, our proposed system leverages the ResNet50 architecture of Convolutional Neural Networks (CNNs) for image classification. This advanced approach enables more accurate and adaptive detection of deepfakes. We trained our model using the Adam optimizer and specific activation functions for feature extraction and classification, achieving an impressive accuracy of 86.92% and a loss of 0.4348, which demonstrates both efficiency and robustness. This system represents a significant advancement in the fight against deepfake content, providing a valuable tool for detecting manipulated images and helping to mitigate the spread of disinformation and propaganda. By offering a reliable defense mechanism against easily accessible and manipulable content on social media, our approach plays a crucial role in maintaining the integrity of digital information.