The rapid development of deepfake technology poses a serious challenge to the legitimacy of digital media and requires effective detection techniques. This chapter explores the challenges and possible solutions of deepfake detection. The challenges encountered involve a lack of a diverse and high-quality dataset to train models, issues with scalability for real-world applications, and constantly evolving deepfake-creation techniques overtaking detection capabilities. Detection also has challenges generalizing to varied types of manipulation and complex in-the-wild scenarios where multiple individuals or changing environments exist. This chapter presents several novel avenues for the resolution of the specified issues. Enhanced cross-forgery detection using advanced architectural designs, like hybrid models combining Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), is realized at a larger scale. Detection accuracy is enhanced by multimodal fusion techniques, which integrate visual, aural, and contextual data. Attention mechanisms and other temporal analyses identify differences within the video sequences. In addition, persistent effectiveness against evolving techniques in deepfake manipulation while improving real-time detection efficiency provides an edge to computing. These research outcomes advance the development and fortification of deepfake detection technologies within a global framework that supports digital trust and curtails the circulation of synthetic media.