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Following PRISMA guidelines, a search was conducted in the Scopus and Web of Science databases, and 80 studies were selected after applying predefined eligibility criteria. These studies were synthesized through quantitative bibliometric methods and structured qualitative thematic categorization. The findings reveal a significant increase in scientific output after 2020, as well as its geographic distribution and potentially the most influential contributions. The main research lines focus on damage detection, deviation detection, and foreign object detection. A clear transition is also observed from traditional image processing methods\u2014such as filtering, segmentation, and geometric analysis\u2014toward deep learning models, including YOLO, CenterNet, and hybrid architectures, with improvements in precision, speed, and stability. Nevertheless, challenges remain related to datasets representativeness, the heterogeneity of evaluation protocols, and variability in operational conditions. Finally, opportunities for advancement are identified through multimodal datasets, adaptive models, and lightweight solutions that facilitate integration into asset management systems and support scalable industrial adoption.<\/jats:p>","DOI":"10.3390\/s26082527","type":"journal-article","created":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T08:24:23Z","timestamp":1776673463000},"page":"2527","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Computer Vision-Based Techniques for Conveyor Belt Condition Monitoring: A Systematic Review"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-2939-0516","authenticated-orcid":false,"given":"Pablo","family":"Rios-Colque","sequence":"first","affiliation":[{"name":"Ingenier\u00eda Mec\u00e1nica-Electromec\u00e1nica-Mecatr\u00f3nica, Facultad Nacional de Ingenier\u00eda, Universidad T\u00e9cnica de Oruro, Oruro, Bolivia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0101-7471","authenticated-orcid":false,"given":"Victor","family":"Rios-Colque","sequence":"additional","affiliation":[{"name":"Ingenier\u00eda Mec\u00e1nica-Electromec\u00e1nica-Mecatr\u00f3nica, Facultad Nacional de Ingenier\u00eda, Universidad T\u00e9cnica de Oruro, Oruro, Bolivia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3408-9233","authenticated-orcid":false,"given":"Luis","family":"Rios-Colque","sequence":"additional","affiliation":[{"name":"Doctorado en Industria Inteligente, Facultad de Ingenier\u00eda, Pontificia Universidad Cat\u00f3lica de Valpara\u00edso, Valpara\u00edso 2340000, Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8312-7554","authenticated-orcid":false,"given":"Pedro A.","family":"Robles","sequence":"additional","affiliation":[{"name":"Escuela de Ingenier\u00eda Qu\u00edmica, Facultad de Ingenier\u00eda, Pontificia Universidad Cat\u00f3lica de Valpara\u00edso, Valpara\u00edso 2340000, Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"10870","DOI":"10.1109\/JSEN.2022.3170971","article-title":"Damage Detection Method of Mining Conveyor Belt Based on Deep Learning","volume":"22","author":"Liu","year":"2022","journal-title":"IEEE Sens. 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