Image forgeries have become much more common due to the widespread use of sophisticated digital image manipulation tools, which makes it difficult to independently confirm authenticity in domains like social media, digital forensics, and journalism. This survey study paper offers a thorough analysis of the most recent methods for identifying image modifications including copy-move and splicing forgeries, with an emphasis on combining Convolutional Neural Networks (CNNs) with Error Level Analysis (ELA). We evaluate important approaches from recent literature based on their architectures, datasets, and performance metrics. We do so via methodically analyzing both contemporary deep learning techniques and traditional methods that rely on handcrafted features. Our investigation shows that hybrid ELA- CNN techniques routinely beat alternative techniques, attaining accuracy rates exceeding 90% on common datasets such as CASIA. More reliable, real-time solutions are required, nevertheless, as problems like identifying subtle tampering and increasing computational effectiveness continue to arise. We wrap up by discussing the present state of the field’s limits and potential avenues for future research in digital image forensics.