Monitoring influenza activity can facilitate developing prevention strategies and optimizing public health resource allocation in an effective manner. Traditional influenza surveillance methods usually have a time lag of 1 to 2 weeks. This study concerns the problem of nowcasting influenza-like illness (ILI) by comprehensively incorporating historical ILI records, Internet search data, and tourist flow information. In this study, a set of predictive models are adapted for ILI prediction, including autoregressive integrated moving average (ARIMA), autoregressive with Google search data (ARGO), extreme gradient boosting (XGBoost), and linear regression (LR). To further improve prediction accuracy, a stacking-based ensemble approach is developed to integrate the prediction results from the different models. These methods are validated using the ILI-related data in Taiwan Province of China at both global and city levels. The results show that the stacking-based ensemble approach achieved the best performance in the task of nowcasting, with the least prediction errors at the global level (MAPE=5.6%; RMSE=0.16%; MAE=0.08%). The developed approach is easily tractable and computationally efficient and can be viewed as a feasible alternative to nowcast ILI in areas where influenza activity has no constant seasonal trend.