The process of determining whether a person is being truthful or not in their verbal or nonverbal conversation is known as lie detection, sometimes referred to as reality verification or deception detection. Deducing the verity of a declaration or claim includes reading various cues consisting of facial expressions, speech patterns, psychological responses, frame language, and different behavioral signs. A progressive method to detect lies by integrating cutting-edge technology like Facial Expression Analysis and Speech Stress Analysis is proposed by this paper. It defines a sophisticated model combining the audio and video RAVDESS dataset, using neural networks for emotion recognition. The architecture of this project entails separate pipelines for audio and video emotion inference, leading to a unified framework that blends emotional insights from both modalities for accurate lie detection. The final result is determined by taking into account the effects of each, the speech and the facial aspect. Rigorous validation against diverse datasets are highlighted as pivotal to ensuring accuracy. Ethical and moral issues are emphasized upon.