I. Introduction
Elevator systems are vertical transportation devices essential for moving people or goods between building levels. They are crucial in high-rise structures, offering efficiency and convenience. Equipped with safety features, elevators are integral to modern urban infrastructure, ensuring accessibility in tall buildings. Elevator systems are ubiquitous component of modern urban infrastructure, have revolutionised vertical transportation in high-rise buildings. The safety, reliability and efficiency of these systems are of paramount concern. Predictive maintenance and reliability analysis are pivotal tools in ensuring the continual operation of elevators [1]. We introduce an innovative approach that leverages Hidden Markov Models (HMMs) [2] to model and predict the dynamic states of elevator systems. Elevator systems are inherently dynamic, operating across a spectrum of conditions. The ability to understand and predict the state of an elevator system whether it is functioning optimally good, experiencing issues or bad or in an intermediate state average is essential for timely maintenance and overall system health management.