<p>Tractor-trailer mass is a crucial factor that affects the stability of vehicle motion control and braking performance. Mass estimation is a valuable technique for ascertaining the mass of vehicles. However, the traditional model-driven approach for estimating tractor-trailer mass suffers from slow convergence and low accuracy. With the advancement of machine learning, data-driven approaches offer a novel solution for tractor-trailer mass estimation. Considering the critical and challenging problems in classical convolutional and cyclic structures, such as inadequate long-term dependency capture and low computational efficiency, an end-to-end tractor-trailer mass estimation method based on a novel time–frequency fusion transformer regression (TFFTR) model is proposed in this paper. The TFFTR regression model consists of three components: a tokenizer, an encoder, and a regressor. Specifically, frequency information of different channels is extracted using the discrete cosine transform and integrated with the corresponding time domain data through element-wise multiplication in the tokenizer. Then, hidden features are extracted in the encoder, and the regressor generates the predicted output. The model was trained and validated using offline, pre-collected real-world tractor-trailer driving datasets. Furthermore, real-vehicle experiments were conducted to validate the effectiveness of the proposed algorithm. This study analyzed the impact of input lengths, frequencies, and characteristics on the model’s performance. The optimal hyperparameters were determined through Bayesian optimization. The experimental results demonstrate that the TFFTR-based method for tractor-trailer mass estimation exhibits superior identification accuracy and achieves state-of-the-art performance.</p>