Addressing the challenge of traditional mathematical model-based quality of transmission (QoT) prediction methods struggling to simultaneously meet the demands of high precision and low computational complexity, this paper introduces three intelligent QoT prediction techniques for single optical paths, multiple optical paths, and cross-topology optical paths. These techniques rely on machine learning models to achieve accurate end-to-end optical path QoT predictions and effectively tackle the following challenges: firstly, how to select appropriate machine learning models and input features amidst the diversity of physical layer parameters. Secondly, how to effectively capture the intricate relationships among optical paths. Thirdly, how to train and continuously optimize network models with limited samples. Finally, the article offers a glimpse into the future development directions of optical path QoT prediction technologies.