2026-03-122026-03-122026-02-27RABELO, Jonas da Silva. Ferramentas No-Code/Low-Code aplicadas ao desenvolvimento de inteligência artificial e de machine learning: uma revisão sistemática da literatura. Orientador: Otávio Noura Teixeira; Coorientadora: Ingrid Nery Mendes. 2026. [27] f. Trabalho de Curso (Bacharelado em Engenharia da Computação) – Faculdade de Engenharia da Computação, Campus Universitário de Tucuruí, Universidade Federal do Pará, Tucuruí, 2026. Disponível em: https://bdm.ufpa.br/handle/prefix/9333. Acesso em:.https://bdm.ufpa.br/handle/prefix/9333This study presents a Systematic Literature Review on the use of No-Code and Low-Code (LC/NC) tools in the development of Artificial Intelligence and Machine Learning solutions. The objective is to map and analyze the platforms employed in this context, identifying their main characteristics, benefits, limitations, and usage trends. The review followed Kitchenham’s guidelines and considered studies published between January 2020 and August 2025 from the ACM Digital Library, IEEE Xplore, ScienceDirect, Scopus, and SpringerLink databases. After applying the inclusion and exclusion criteria, 67 studies were selected. The results indicate that LC/NC tools broaden access to AI and ML development, particularly for users without advanced technical backgrounds. However, limitations related to flexibility, advanced customization, and ecosystem dependency were identified, along with a predominance of descriptive and feasibility studies.Acesso AbertoLow-CodeNo-CodeInteligência artificialAprendizado de máquinaRevisão sistemáticaArtificial intelligenceMachine learningSystematic reviewCNPQ::ENGENHARIASCNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::METODOLOGIA E TECNICAS DA COMPUTACAOTrabalho de Curso - Graduação - ArtigoAttribution-NonCommercial-NoDerivs 3.0 Brazil