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The Rapid Advancement Of Artificial Intelligence And Generative Models Has Led To The Rise Of Deepfakes—synthetically Altered Or Fabricated Audio, Video, And Images That Are Increasingly Indistinguishable From Authentic Content. While Deepfake Technology Has Promising Applications In Entertainment, Education, And Creative Industries, It Also Poses Severe Threats To Privacy, Security, Politics, And Digital Trust When Misused For Disinformation, Identity Theft, Or Fraud. Traditional Detection Methods, Such As Manual Inspection And Handcrafted Feature-based Techniques, Are Insufficient Against The Sophisticated Manipulations Generated By Modern Deep Learning Architectures Like Generative Adversarial Networks (GANs) And Autoencoders. To Address These Challenges, Deep Learning-based Detection Techniques Have Emerged As A Powerful Solution Due To Their Ability To Automatically Learn Discriminative Features From Large-scale Datasets. Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), And Transformer-based Architectures Have Been Widely Employed To Capture Spatial, Temporal, And Frequency Inconsistencies Present In Forged Media. Additionally, Hybrid Models That Integrate Multimodal Analysis—combining Visual, Audio, And Physiological Cues—have Shown Significant Improvements In Detection Accuracy. This Study Explores Recent Advancements In Deepfake Detection Using Deep Learning, Highlighting Key Methodologies, Benchmark Datasets, Performance Metrics, And Ongoing Challenges Such As Generalizability, Adversarial Attacks, And Real-time Implementation. The Findings Emphasize The Critical Role Of Robust And Adaptive Detection Systems In Safeguarding Digital Media Integrity And Mitigating The Societal Risks Associated With Deepfakes.