Current aircraft engine remaining useful life prediction methods often rely on a holistic analysis of multi-source sensor data, typically using a single time scale or focusing on spatial features, which neglects key differences in sensor data at different time points. To address these limitations, a novel multi-scale temporal convolutional network (MTCN) is proposed to comprehensively extract both long-term and short-term temporal features from multi-source sensor data. Additionally, a dual attention mechanism, integrating channel attention and self-attention, is designed to enhance spatial feature representation and selectively focus on critical sensor measurements at key time points. The collaborative integration of MTCN and the dual attention mechanism facilitates effective spatiotemporal feature fusion, improving the model’s capacity to capture complex degradation patterns. Moreover, the Gaussian error linear unit (GeLU) activation function is employed to enhance the network’s nonlinear fitting capability. Experimental evaluations conducted on the NASA C-MAPSS benchmark dataset demonstrate that the proposed method significantly outperforms state-of-the-art approaches, achieving average reductions of 7% in root mean square error (RMSE) and 13.1% in Score, thereby verifying its superior prediction accuracy and robustness.