Given the increasing prevalence of deepfake technology, this paper presents a comprehensive study on applying deep voice recognition methods for effective deepfake detection. We explore techniques for differentiating authentic and fabricated audio recordings, including Long Short-Term Memory (LSTM)-based models and the WIRENetSpoofImprovedEnhanced architecture. Our experiments involve natural and synthetic speech samples tested on a balanced dataset sourced from Kaggle. Through detailed evaluation and analysis, we demonstrate that our proposed approach effectively combats the spread of deepfake audio content. While visual deepfake detection has significantly progressed, detecting manipulated audio remains challenging. Our research aims to enhance automated systems’ ability to identify and mitigate the distribution of deepfake content by focusing on key characteristics that separate genuine from altered voice samples. This study contributes significantly to the ongoing efforts to safeguard the integrity of media and digital communication.