The multi-target assignment (MTA) problem, a crucial challenge in command control, mission planning, and a fundamental research focus in military operations, has garnered significant attention over the years. Extensively studied across various domains such as land, sea, air, space, and electronics, the MTA problem has led to the emergence of numerous models and algorithms. To delve deeper into this field, this paper starts by conducting a bibliometric analysis on 463 Scopus database papers using CiteSpace software. The analysis includes examining keyword clustering, co-occurrence, and burst, with visual representations of the results. Following this, the paper provides an overview of current classification and modeling techniques for addressing the MTA problem, distinguishing between static multi-target assignment (SMTA) and dynamic multi-target assignment (DMTA). Subsequently, existing solution algorithms for the MTA problem are reviewed, generally falling into three categories: exact algorithms, heuristic algorithms, and machine learning algorithms. Finally, a development framework is proposed based on the ''HIGH'' model (high-speed, integrated, great, harmonious) to guide future research and intelligent weapon system development concerning the MTA problem. This framework emphasizes application scenarios, modeling mechanisms, solution algorithms, and system efficiency to offer a roadmap for future exploration in this area.