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The reliability and efficiency of permanent magnet synchronous motors (PMSMs) depend on accurate rotor temperature estimation, for which advanced data-driven models have been developed recently. These models, which are essentially constructed around the concepts of machine learning dilemmas, suffer from a major shortcoming. That is, even though they demonstrate encouraging results for data samples picked from distributions within a source domain, they are unable to generalize to a target domain with a different distribution. Consequently, it is of paramount importance for an estimation model to perform reliably under unseen operating conditions as well. This work is devoted to designing a novel, lightweight yet efficient deep learning framework for real-time rotor temperature estimation. To enable the model to learn domain-invariant features, we leverage gradient reversal domain adaptation into the training of baseline deep learning models. This is to make the models generalize well to diverse operating conditions and to ensure robust performance under unseen scenarios. The trained models are deployed on a microcontroller in an experimental PMSM setup to demonstrate the feasibility of the framework in a resource-constrained embedded motor control system. The effectiveness of the proposed method is further validated for an open-source real dataset. The attained results demonstrate that the trained models can achieve mean absolute temperature estimation errors as low as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$0.69^{\circ }\mathrm{C}$</tex-math></inline-formula> for unseen data.