Deepfakes are an emerging threat to digital authenticity and security, hence a proper detection technique needs to be created in order to establish public study confidence. A thorough roadmap to the development of deepfake detection software has been provided in this paper, reviewing the state-of-the-art algorithms, such as XceptionNet, EfficientNet, and hybrid models integrating spatial and temporal analysis. It provides methodologies for implementation, data preprocessing, and software pipeline development, serving as a practical guide to researchers and developers. Theoretical study to application-oriented practice closes the gap in terms of bottom line development and adaptive detection systems addressing the growing menace of deepfake media.