The research proposes a distinctive method to identify unauthorized people who enter restricted areas through a combination of K-LD7 millimeter wave radar systems and deep learning algorithms. Gait patterns obtained from Doppler and micro-Doppler signals are analyzed by the system which offers both privacy preservation and non intrusiveness as opposed to conventional methods like CCTV surveillance. The Random Forest Classifier shows excellence by accurately identifying authorized or unauthorized individuals at a rate of 82% while maintaining its capabilities during various challenging environmental situations. The solution provides high practicality when used for real-time monitoring deployments. Future development efforts will direct their attention to growing the dataset while making the solution work efficiently on edge computing devices.