This paper presents a novel approach to detecting cheating during online interviews by leveraging multimodal machine learning techniques. By analyzing audio and video data, we identify key features indicative of dishonest behavior, such as voice tone, pitch, stress levels, background noise, eye movements, gaze features, and head movements. We collected and meticulously labeled a comprehensive dataset to train our models. Our audio models examine voice inconsistencies and background noises, while video models monitor eye and head movements to detect off-screen distractions. These techniques ensure a more accurate and reliable evaluation of candidates’ true skills. Compared to traditional proctoring methods, our machine learning models offer significant advantages, including scalability, non-intrusive monitoring, real-time analysis, and cost-effectiveness. Furthermore, these models enhance accuracy through diverse data point analysis and adapt to evolving online cheating tactics, all while respecting candidates’ privacy and providing a more comfortable interview experience. Our approach demonstrates a robust and effective method to maintain the integrity of online interview processes.