The exponential growth of smart devices and advanced image editing tools has made detecting and localizing image forgeries critical for ensuring digital content integrity. This paper focuses on developing a robust and scalable model for passive image forgery detection using convolutional neural networks (CNNs). Leveraging datasets like CASIA1 and MICC-F220, the study aims to identify tampered regions in digital images by analysing noise patterns, pixel-level anomalies, and compression artefacts. The proposed methodology integrates pre-processing, model training, and validation using diverse datasets to enhance detection accuracy and scalability. Compared to traditional techniques, the deep learning-based approach shows significant improvements in detecting complex forgeries, including splicing and copy-move manipulations. Applications of this research extend to digital forensics, media authentication, and cybersecurity. The findings underscore Deep learning systems’ show promise to tackle new issues in picture forgery detection and localization.