This paper introduces UniOcc, a new benchmark for 3D occupancy forecasting and prediction in autonomous driving. It unifies data from real-world datasets like nuScenes and Waymo with synthetic data from CARLA and OpenCOOD, providing 2D/3D occupancy labels and voxel-level flow. UniOcc also offers novel evaluation metrics that don't rely solely on imperfect ground truth and includes a toolkit for object segmentation and tracking within the voxel space, aiming to standardize and advance research in this critical area.