Deepfakes, produced by deep learning algorithms, are a growing concern in the digital revolution. They create misleading content, impacting politics and individual privacy. This study examines deepfake generation using deep learning, focusing on algorithmic foundations, ethical implications, and architectural comparisons. Deepfakes leverage Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNNs), with advanced variants like StyleSwin and Hit-l enhancing quality. Ethically, deepfakes raise concerns about misinformation, privacy, and security, necessitating regulation. The study compares transformer-based (StyleSwin, Stylenat, Hit-l) and Convolution-based (StyleGAN2, StyleGAN3, StyleGAN XL) architectures using the FFHQ dataset at 256 and 1024-pixel resolutions. Findings show transformers perform better at lower resolutions (256 pixels), capturing fine details and spatial coherence, while Convolutions excel at higher resolutions (1024 pixels), ensuring better visual fidelity. The optimal architecture depends on resolution requirements, with transformers suited for low-resolution accuracy and Convolutions for high-resolution detail.