With the rapid advancement of deepfake audio technology, producing artificial voices that sound remarkably realistic is now simpler than ever. As this becomes available In addition to opening up new creative possibilities, it also poses grave security, disinformation, and fraud concerns. Although many of the deepfake audio detection tools available today are accurate, their application in real-time situations is limited by their high processing overhead. In this work, we introduce a Convolutional Neural Network (CNN)-based lightweight deepfake audio detection model. Our model can detect AI-generated speech accurately and with minimal processing demands by utilizing Mel-Spectrogram features and an effective CNN architecture. The model works well with high accuracy and low latency, according to tests on standard datasets, which makes it appropriate for real-world uses like media verification and voice authentication. Looking ahead, we aim to improve the model's ability to withstand adversarial attacks and adapt to new types of deepfake generation techniques. In the future, we would like to improve the model's robustness against hostile attacks and make it more flexible to accommodate new deepfake generation techniques.