The proliferation of voice-activated technologies and the increasing sophistication of AI-generated voice clones pose significant security challenges. Speaker identification, despite advancements in automatic speech recognition (ASR) and natural language processing (NLP), requires more robust authentication mechanisms. This paper explores the potential of deep learning models to distinguish between authentic human speech and AI-generated voice clones. Due to the limited availability of AI-generated voice datasets, we created a custom dataset using both commercial and open-source voice cloning tools. We employed a Convolutional Neural Network (CNN) combined with a Gated Recurrent Unit (GRU) to classify voice samples as authentic or AI-generated. Our results demonstrate the potential of deep learning in detecting AI voice clones, providing a foundation for future research into more comprehensive and secure speaker authentication methods.