This research paper introduces an innovative paradigm for predictive maintenance in induction motors, underpinned by the development of a 3D holography-based digital twin. Digital twin technology has revolutionized product manufacturing and real-time analysis, allowing engineers and scientists to seamlessly engage with virtual replicas of mechanical systems in real-time, eliminating the need for Virtual or Augmented Reality (VR/AR) headgear. The incorporation of 3D holographic projection enriches this experience, providing an immersive grasp of the system’s complexities. Grounded in the principles of interference and diffraction, holographic projection technology excels in capturing and reproducing lifelike 3D images, immersing users within a compelling virtual environment. In this project, we advocate the implementation of an Extended Kalman Filter (EKF) digital twin model to attain precise state estimation within a speed sensor-less rotor field-oriented controlled induction motor drive. The EKF digital twin model, serving as a mathematical representation of the physical system, harnesses the core tenets of the EKF to augment our comprehension of the motor’s behavior, thus facilitating enhanced control and analysis. To affirm the efficacy of the EKF digital twin model, comprehensive simulations are executed using MATLAB, Simulink. The integration of 3D holography-based digital twin technology in tandem with the EKF model assembles a resilient and versatile toolkit for predictive maintenance and efficient monitoring of electric drive systems. This progressive approach holds the potential to substantially elevate the reliability and performance of induction motors, marking a significant stride in the realm of electrical engineering and predictive maintenance.