In motion detection systems leveraging mmWave radar, false alarms triggered by pets, foliage, or even environmental disturbances have long been a pain point, leading to inefficiencies in energy management (for example, thermostats activating unnecessarily) or security systems generating unwarranted alerts. By integrating AI into mmWave radar pipelines, these challenges are systematically addressed. Trained in vast datasets of human movement patterns and signal profiles from non-human objects, AI algorithms can dissect high-fidelity data of the mmWave in real time, distinguishing between meaningful human presence and irrelevant noise with remarkable precision. This not only elevates user experience by eliminating disruptive false triggers but also optimizes system reliability, making sure that applications such as smart home automation, commercial occupancy sensing, or security designs operate with unwavering accuracy, even in complex and dynamic environments.