The rapid advancement of Artificial Intelligence, particularly deep learning and generative adversarial networks (GANs), has enabled the creation of highly realistic synthetic multimedia content known as deepfakes. Deepfake technologies can manipulate images, videos, and audio recordings with remarkable realism, creating significant challenges for digital security, media authenticity, public trust, and information integrity. Deepfakes pose serious threats in areas such as social media, political communication, financial transactions, cybersecurity, and digital forensics. Consequently, robust detection mechanisms are required to identify manipulated content accurately and efficiently. Signal processing techniques have emerged as a powerful approach for detecting deepfakes by analyzing hidden artifacts, frequency-domain inconsistencies, temporal anomalies, and biometric characteristics embedded within multimedia signals. This paper presents a comprehensive study of Deepfake Detection Using Signal Processing and proposes an Artificial Intelligence-Driven Signal Processing Framework (AI-SPF) designed to enhance deepfake identification accuracy. The proposed framework integrates signal preprocessing, frequency-domain analysis, feature extraction, machine learning classification, and adaptive forensic verification mechanisms. Experimental evaluation demonstrates significant improvements in detection accuracy, false positive reduction, and computational efficiency compared with traditional forensic approaches. The findings indicate that signal processing-based deepfake detection will become an essential component of future cybersecurity and digital media authentication systems.