Deception detection is an important field of research with applications in law enforcement, corporate security, and forensic research. Traditional methods, such as polygraphs, often fail due to their limited reliability, so advanced solutions are needed. In this context, various tools have been developed in recent years that use artificial intelligence to analyze deception through verbal, non-verbal, and physiological signals. Artificial intelligence approaches include deep learning and machine learning, which consider facial expressions, speech patterns, and body movements to improve accuracy and reliability. This review provides an overview of the effectiveness of state-of-the-art AI methods, including new techniques such as adversarial learning, spatial–temporal modeling, and multimodal fusion. This work highlights how datasets such as real-life trials can be used to train AI models to perform deception detection tasks with high accuracy—over 90% in various scenarios. While AI is promising, it also faces challenges. For example, the misuse of AI for deception raises ethical and legal issues. The study points to the dual potential of AI in detecting and producing deception and emphasizes the ongoing need for innovation, comprehensive datasets, and robust ethical frameworks, while mitigating the risks and maximizing the benefits of such technologies.