A.Ashok ,Dr G.Vijaya Kumar 03 06 The emergence of deep learningbased speech synthesis technologies has enabled the generation of highly realistic artificial audio commonly known as deep fake audio These synthetic audio signals can imitate human voices with remarkable accuracy posing significant threats in areas such as identity theft misinformation financial fraud and digital forensics Traditional detection methods struggle to identify subtle artifacts present in deep fake audio due to the increasing sophistication of generative models like WaveNet Tacotron and GANbased architecturesThis paper proposes an advanced deep learningbased framework for detecting deep fake audio using hybrid neural network architectures The system integrates Convolutional Neural Networks CNNs for spatial feature extraction and Long ShortTerm Memory LSTM networks for temporal sequence modeling Audio signals are transformed into timefrequency representations such as spectrograms and MelFrequency Cepstral Coefficients MFCCs to capture discriminative patterns The model is trained and evaluated on benchmark datasets like ASVspoof achieving high detection accuracy and robustness across diverse spoofing attacks Experimental results demonstrate that the proposed approach significantly outperforms traditional machine learning techniques This research contributes to enhancing audio authentication systems and mitigating risks associated with synthetic media Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026