In recent years, rapid advancements in Artificial Intelligence and Computer Vision have enabled the creation of highly realistic synthetic media known as deepfakes. While these technologies offer valuable applications in entertainment and digital content creation, they also pose serious risks including misinformation, impersonation, identity fraud, and political manipulation. This paper presents a comprehensive review of deepfake generation techniques and detection methodologies. Various deep learning approaches such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM) are analyzed alongside traditional machine learning techniques. A structured taxonomy for deepfake detection based on spatial, temporal, and frequency-domain features is proposed. The study highlights current challenges, research gaps, and future directions toward building reliable and scalable deepfake detection frameworks for real-world applications.