The proliferation of deepfake technology poses a significant threat to the veracity of multimedia content, necessitating robust countermeasures for detection. This research explores the efficacy of three distinct deep learning architectures—Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN)—in discerning deepfake manipulations within video data. The study encompasses a diverse dataset encompassing real and manipulated videos, ensuring comprehensive evaluation. Each model is meticulously trained and rigorously tested, with performance assessed in terms of accuracy, precision, recall, and F1-score. The results underscore the CNN’s exceptional prowess in spatial feature extraction, yielding superior accuracy compared to its temporal counterparts. A comprehensive discussion delves into the strengths and limitations of each model, providing valuable insights for future research in this critical domain. This research not only contributes to the arsenal of techniques for deepfake detection but also underscores the dynamic landscape of multimedia forensics, necessitating continuous innovation to safeguard the integrity of visual content in an era of increasingly sophisticated manipulations. The findings herein offer a foundational framework for further advancements in deepfake detection methodologies.