Climate change poses a significant global challenge that necessitates concerted efforts toward carbon neutrality. Circulating fluidized bed (CFB) boilers have gained prominence in various industries due to their adaptability and reduced emissions. However, many current control systems rely heavily on manual operator intervention and lack advanced automation, which constrains the operational efficiency. This study addressed the need for dynamic models capable of monitoring and optimizing NOx emissions in CFB boilers, especially under fluctuating loads and strict regulatory standards. We introduced the TimesNet model, which utilizes fast Fourier transform (FFT) to extract key frequency components, transforming 1D time series data into 2D tensors for enhanced feature representation. The model employs Inception blocks for multi-scale feature extraction and incorporates residual connections with amplitude-weighted aggregation to mitigate catastrophic forgetting during training. The results indicated that TimesNet achieved R2 values of 0.98, 0.97, and 0.95 across training, validation, and testing datasets, respectively, surpassing conventional models with a reduced MAE of 1.63 mg/m3 and RMSE of 3.35 mg/m3. Additionally, it excelled in multi-step predictions and effectively managed long-term dependencies. In conclusion, TimesNet provides an innovative solution for the precise monitoring of NOx emissions in CFB boilers by enhancing predictive stability and robustness and addressing salient limitations in existing models to optimize combustion efficiency and regulatory compliance.