Abstract: Seismic imaging plays an important role in the discovery of deep discontinuities, mineral exploration, oil and gas prospecting and development, as well as geological surveys. Over the past half-century, with the rapid advancement of high-performance computing and advanced data acquisition, seismic imaging methods have undergone an evolution from traditional ray-based migration to wave-equation imaging, and further to least-squares migration (LSM) and full-waveform inversion (FWI) imaging. By solving a linear or nonlinear inversion problem, inversion-based migration estimates a generalized inverse of the subsurface reflectivity model, which overcomes the limitations of conventional adjoint-operator-based migration methods in irregular acquisition, limited-bandwidth data, and unbalanced illumination. It can significantly enhance imaging resolution and amplitude fidelity. We systematically review the progress and cutting-edge developments of inversion-based migration in exploration seismology, especially focusing on the theory and methodology of data-domain, image-domain and intelligent LSMs. Additionally, we describe various regularization and preconditioning strategies for LSM in terms of their mathematical principles and practical effectiveness. Finally, we discuss the latest development in nonlinear FWI imaging, offering theoretical and methodological references for high-precision seismic imaging studies.