Deepfake refers to technologies that, using deep learning and neural networks, manipulate audio, visual, or video content in a way that appears real but is entirely fabricated. This study, employing a descriptive-analytical method, examines the legal challenges of judicial proceedings in cases arising from crimes committed through deepfakes. The necessity of this research stems from the unprecedented challenges faced by judicial systems in determining criminal liability, proving the mental element, and validating evidence, where traditional approaches in criminal law are insufficient to address these complexities. Findings indicate that confronting these challenges requires the development of novel legal-technical frameworks. These frameworks should include drafting specialized laws for deepfake-related crimes, applying strict liability for producers, enhancing transparency of system operations through techniques such as digital watermarking, and providing judicial training and expertise in the field of deepfakes. The present study emphasizes that an intelligent integration of legal principles, advanced monitoring technologies, and ethical frameworks can create a safer and more equitable digital ecosystem. This integrated approach not only protects individual rights but also provides the necessary space for the growth of transformative technologies and can serve as a model for addressing future challenges posed by emerging technologies.