Accurate bus travel time prediction is crucial for improving travel experience, especially in transfer journeys. This study introduces a novel multi-route bus travel time prediction system-based PMLNet, a partition and combination prediction framework, addressing the gap in accurate prediction models by incorporating macro and local impact factors. The system employs a pre-processing algorithm for constructing travel chains, partitions travel time into four components, utilizes LSTM along with the newly proposed MDARNN model for predicting each component, and applies four real-time traffic impact factors to calibrate the predictions of each component. Experimental validation on four bus routes demonstrates PMLNet’s superior performance, achieving mean absolute percentage errors (MAPE) as low as 2.91% and mean absolute errors (MAE) below 1.45 min, outperforming traditional models and various partitioned combination frameworks. These findings underscore PMLNet’s potential to significantly improve public transportation services by providing more accurate travel time predictions, ultimately enhancing the user experience in intelligent transportation systems.