In the digital twin modeling of aero-engine turbine blades, traditional CFD requires extensive computational time. Existing data-driven models also struggle to achieve multi-physics decoupling and high-accuracy predictions with extremely small sample constraints. To overcome these challenges, a multitask generative adversarial network (M-GAN) is proposed for multi-physics digital twin modeling. By incorporating a multi-branch attention mechanism into the U-Net generator, this method achieves the decoupling and prediction of multi-physics fields. Additionally, a multi-objective adaptive loss optimization strategy based on uncertainty weighting is proposed to mitigate gradient interference and mode collapse during the joint training of multiple coupled physical fields. Based on the proposed M-GAN, a high-performance digital twin model for turbine blades is developed using only 10 training samples, achieving accurate predictions of pressure, temperature, and airflow angle fields. The test results demonstrate that, on a test set comprising 90 different operating conditions, the average prediction error across all physical fields is only 1.85%, indicating strong cross-physics prediction capability, generalization, and robustness. This approach provides a solid algorithmic foundation for real-time, high-precision mapping in turbine blade digital twin systems.