Purpose <p>Rotating systems are one of the most common systems that transfer power in several industrial sectors. Unbalance is a severe fault that contributes to machine downtime and unscheduled maintenance actions and can damage crucial rotary systems. Estimation of unbalance and model parameter update in rotor-bearing systems are crucial for the safe and efficient operation of the machine.</p> Method <p>This research paper presents a novel approach utilizing artificial neural networks (ANNs) to identify the mass and location of unbalance in a multidisk system. Additionally, the study explores the model updating process of rotating system parameters using genetic algorithms (GAs).</p> Results <p>The integration of physics-based models with artificial intelligence algorithms led to the development of digital twin-based methodologies for model parameter updating and unbalance estimation. For all the artificial neural networks of this paper, the coefficient of determination (R²) exceeded 0.99, indicating excellent predictive accuracy. Additionally, the implementation of genetic algorithms (GAs) consistently yielded objective function values below 5 × 10^–4, enhancing the reliability of the developed systems. These results demonstrate the system’s potential for real-time application and its significant advantages in predictive maintenance.</p> Conclusion <p>The main novelty of this work is the development of digital twin methodologies, for unbalance estimation and parameter assessment, which consist of a mathematical model of the rotor, ANNs, GAs, and real-experimental vibration data from a rotor-kit. The obtained results demonstrate the system’s potential for real-time application and its advantages in predictive maintenance.</p>