<p>Breakthroughs in high-definition metrology have propelled the characterization of engineering surface topography from qualitative description to quantitative analysis. Surface topography filtration, as a core technology for separating multiscale features, has seen its methodological innovations and application expansions become a critical demand in precision manufacturing and advanced materials. This paper systematically reviews the evolution of engineering surface topography filtration technology: from traditional linear methods to nonlinear multiscale analysis techniques, to geometry-aware filtering, and finally to cutting-edge methods driven by artificial intelligence. It reveals a progressive paradigm shift from “noise suppression” to “feature enhancement” and “function awareness”. For typical engineering surfaces, including continuous flat, multi-hole discontinuous, and freeform surfaces, this paper presents a comparative analysis of the application scenarios and inherent limitations of various filtering methods. It focuses on the breakthrough progress of artificial intelligence in automating boundary detection, intelligently generating rough surfaces, and enhancing model interpretability. The study indicates that the field currently faces several core challenges in cross-scale feature characterization, computational efficiency, standardization, and mapping mechanisms for topography and function. Looking ahead, the field will focus on the deep integration of physical mechanisms with eXplainable artificial intelligence (XAI) and advanced data models. The ultimate aim is to establish reliable quantitative relationships between topography and function, thereby evolving filtration technology from a passive post-processing tool into a core enabling technology that supports predictable design and controllable manufacturing.</p>