Single-shot 2-dimensional optical imaging of transient phenomena is indispensable for numerous areas of study. Among existing techniques, compressed ultrafast photography (CUP) using a chirped ultrashort pulse as active illumination can acquire nonrepetitive time-evolving events at hundreds of trillions of frames per second. However, the bulky size and conventional configurations limit its reliability and application scopes. Superdispersive metalenses offer a promising solution for an ultracompact design with a stable performance by integrating the functions of a focusing lens and dispersive optical components into a single device. Nevertheless, existing metalens designs, typically optimized for the full visible spectrum with a relatively low spectral resolution, cannot be readily applied to active-illumination CUP. To address these limitations, here, we propose single-shot compressed ultracompact femtophotography (CUF) that synergically combines the fields of nanophotonics, optical imaging, compressed sensing, and deep learning. We develop the theory of CUF’s data acquisition composed of temporal–spectral mapping, spatial encoding, temporal shearing, and spatiotemporal integration. We also develop CUF’s image reconstruction via deep learning. Moreover, we design and evaluate CUF’s crucial components—a static binary transmissive mask, a superdispersive metalens, and a 2-dimensional sensor. Finally, using numerical simulations, CUF’s feasibility is verified using 2 synthetic scenes: an ultrafast beam sweeping across a surface and the propagation of a terahertz Cherenkov wave.