Determining the tunnel portal boundary mileage is one of the key parameters in tunnel design. Accurately predicting the tunnel portal boundary mileage is significant for enhancing tunnel portal design efficiency. Methods for constructing two features based on discrete terrain point cloud data and tunnel position information are proposed in this study. Furthermore, two features are uniformly extracted along the tunnel design alignment, forming temporal evolution features. Moreover, three AI algorithms (LSTM, GRU, TCN) are selected to establish the prediction model of tunnel portal boundary mileage based on the optimal hyperparameters obtained through a five-fold cross-validation and grid search algorithm. The research results reveal that the predictive accuracy of the LSTM, GRU, and TCN network models on the training sets are 88%, 87%, and 55%, respectively. The tunnel portal boundary mileage prediction models established by the LSTM and GRU algorithms demonstrate better predictive performance. When applied to a new dataset, it is found that the predictive accuracy of the LSTM and GRU models is 86%. Therefore, the LSTM and GRU models are suitable for predicting the tunnel portal boundary mileage. The research offers valuable guidance for designers in selecting tunnel portal boundary mileage and provides a novel approach for predicting relevant parameters in tunnel design using AI technologies.