Hamdan, Mutasem Q.Lee, HaeyoungTriantafyllopoulou, DionysiaBorralho, RubenKose, AbdulkadirAmiri, EsmaeilTafazolli, Rahim2025-09-252025-09-2520231424-8220https://doi.org/10.3390/s23218792https://hdl.handle.net/20.500.12573/4548Zitouni, Rafik/0000-0002-1675-9180; Hamdan, Mutasem/0000-0003-2331-4021; Pozza, Riccardo/0000-0002-8025-9455; Chen, Gaojie/0000-0003-2978-0365; Kose, Abdulkadir/0000-0002-6877-1392; Lee, Haeyoung/0000-0002-5760-6623; Amiri, Esmaeil/0009-0006-3520-6350; Triantafyllopoulou, Dionysia/0000-0002-8150-4803; Heliot, Fabien/0000-0003-3583-3435; Bagheri, Hamidreza/0000-0002-4372-0281The evolution of network technologies has witnessed a paradigm shift toward open and intelligent networks, with the Open Radio Access Network (O-RAN) architecture emerging as a promising solution. O-RAN introduces disaggregation and virtualization, enabling network operators to deploy multi-vendor and interoperable solutions. However, managing and automating the complex O-RAN ecosystem presents numerous challenges. To address this, machine learning (ML) techniques have gained considerable attention in recent years, offering promising avenues for network automation in O-RAN. This paper presents a comprehensive survey of the current research efforts on network automation usingML in O-RAN.We begin by providing an overview of the O-RAN architecture and its key components, highlighting the need for automation. Subsequently, we delve into O-RAN support forML techniques. The survey then explores challenges in network automation usingML within the O-RAN environment, followed by the existing research studies discussing application of ML algorithms and frameworks for network automation in O-RAN. The survey further discusses the research opportunities by identifying important aspects whereML techniques can benefit.eninfo:eu-repo/semantics/openAccessOpen Radio Access NetworksMachine LearningArtificial IntelligenceArticle10.3390/s232187922-s2.0-85176899516