This paper explores the significance of Quality of Transmission (QoT) estimation in optical networks and highlights the increasing use of machine learning (ML) techniques to enhance QoT estimation accuracy. It presents a survey of literature in this area, categorizing studies into classification and regression algorithms. ML methods are shown to improve QoT estimation, mitigate nonlinearities, and optimize decisionmaking processes. Ultimately, these advancements reduce the reliance on conservative margins, maximize network capacity, and decrease infrastructure investment. Accurate and real-time QoT information is the foundation for efficient routing and spectral allocation (RSA) systems, it enables proactive failure management, facilitates network reconfiguration, provides inputs for optical line optimization and drives optical network automation.