Thermal errors in machine tools are responsible for up to 75% of overall inaccuracy, leading to compromised product quality, increased scrap rates, and higher production costs. These challenges become even more pressing given the adverse effects towards thermal stability from modern trends in machine tool (MT) design, such as increased productivity demands, modular machine architectures, the integration of multiple production technologies into a single platform, and the push for resource efficiency. Although thermal error compensation has been recognized as a cost-effective means of enhancing precision, current solutions still face major hurdles. Chief among them are the reliance on extensive on-machine calibration and the difficulty of transferring compensation models to new or different machines without time-consuming re-tuning. This project proposes an intelligent thermo-mechanical (T-M) digital twin software option (T-M AI DigiTwin), merging physics-based simulation with data-driven modelling for robust, scalable thermal error compensation. By integrating numerical simulations—refined through projection-based model order reduction (MOR)—with real-world measurements, we will reduce the need for extensive on-machine data collection and calibration. This hybrid system is designed to adapt seamlessly, boosting precision without imposing lengthy or costly pre-production procedures. On the simulation side, finite element—accelerated through MOR—support near-real-time predictions under varying loads. These synthetic datasets help pretrain machine learning models, which are then fine-tuned using smaller but more relevant on-machine datasets. This ensures each digital twin matches the specific thermal profile and production environment of the actual machine. Complementing these simulations, an experimental data-driven model uses continuous sensor input (e.g., spindle and axis temperatures, positional deviations), facilitated by on-machine measurement (OMM). An AI-based Adaptive Learning Control module fuses these streams, maintaining or improving compensation accuracy as operating conditions evolve over the machine’s lifecycle. Pilot implementations across different industrial settings will demonstrate how the T-M AI DigiTwin can cut downtime, reduce scrap, and increase overall cost efficiency. By advancing the adoption of digital, data-centric solutions, this project will not only strengthen manufacturer competitiveness and sustainability but also serve as a catalyst for future innovations in machine tool accuracy—benefiting a broad spectrum of sectors, from automotive and aerospace to medical and semiconductor manufacturing.