Environmental, Social, and Governance (ESG) indicators are core metrics for evaluating corporate sustainability and long-term resilience. Against the backdrop of escalating climate risks and increasingly stringent environmental regulations, timely and reliable forecasting of the environmental (E) dimension has become critical—yet remains challenging in the presence of abrupt structural changes. Using the Huazheng ESG Ratings dataset, which covers 2,270 mainland A-shares and Hong Kong–listed companies from 2013 to 2022, we formulate E-score prediction as a multivariate annual time-series task and generate training samples via overlapping sliding windows. We propose the Mutation-Aware CNN-Transformer (MACT), the first hybrid architecture explicitly designed to model ESG “mutations.” MACT employs convolutional encoders to capture short-term patterns, Transformer blocks to learn long-range dependencies, and two mutation-aware augmentation strategies—synthetic mutation injection and temporal masking—that introduce sudden shocks and missing segments during training. Extensive experiments show that MACT reduces the Root Mean Square Error (RMSE) to 3.7951 and the Mean Absolute Percentage Error (MAPE) to 3.67%. These results correspond to a reduction in RMSE and MAPE of 34.02% and 46.73%, respectively, compared to the state-of-the-art (SOTA) model Long Short-Term Memory (LSTM), and an improvement of 34.96% and 46.89% relative to the Transformer baseline. Our findings demonstrate that integrating convolutional feature extraction, attention-based sequence modeling, and mutation-aware augmentation yields a highly accurate and robust framework for forecasting corporate environmental performance.