In recent years, fiber-optical distributed acoustic sensing (DAS) has been applied to various large-scale infrastructure monitoring areas in smart cities, leading to a new generation of fiber-optic IoT for ground listening. However, its single-task-focused postprocessing methods cannot achieve real-time efficient ground event recognition and localization concurrently. In this article, a two-level multitask learning (MTL) enhanced smart fiber-optical DAS (sDAS) system is proposed, for the first time, to simultaneously realize ground event recognition and localization. Performances and efficiency of both tasks are significantly improved by sharing knowledge across them. Besides, the imbalanced incremental learning ability for new events is also enhanced in the proposed MTL network. The total computation time for the two tasks is greatly shortened to 0.3 ms for a spatial-temporal sample with 129-m fiber length and 5-s time frame, which equals to a processing time of 0.04 s over a total fiber length of 18.7-km with a spatial sampling interval of 1.29 m, and is even better than the fastest single recognition reported to date. In the field test, such an MTL-enhanced sDAS system indicates excellent feature extraction performance with classification accuracy of up to 99.46% for five events and location error of ±1 m for two core-events at 8/16 different radial distances, which are much better than the DAS systems with multiclassifier and the combined single-task learning methods. Also, the MTL-enhanced sDAS shows strong robustness against environmental noises. Hence, it provides a breakthrough technology for time-efficient multitask processing in smart distributed sensors.