In seismic exploration, complex seismic wave fields comprising reflected waves, scattered waves, and other phenomena are formed due to the intricate nature of underground structures. Traditional imaging methods typically focus solely on the reflected wave field, disregarding the scattered wave field. This limitation hampers accurate imaging of small-scale structures and impedes the identification of complex structures. To address this challenge and achieve precise imaging of small-scale structures, it is crucial to separate from the scattered waves from the seismic wave field. Among the various wave field separation algorithms, filtering-based methods have shown promising results in accurately extracting scattered waves and enhancing imaging resolution. This study explores and summarizes different methods for seismic scattered wave separation based on filtering techniques. By reviewing the research findings of both domestic and international scholars in the field of filtering-based scattered wave separation, the study provides an overview of the progress made and compares and analyzes the separation effects of each method. Additionally, considering the advancements in deep learning within the realm of artificial intelligence, the future development direction of filtering-based scattered wave separation is also envisioned.