This research investigates the use of large language models and machine learning techniques to identify the primary triggers for air traffic flow management regulations. The study focuses on textual remarks made by flow managers who implemented these regulations. The investigation takes a concrete form by using weather-related regulations with the referenced location being an aerodrome. Specifically, a large language model is asked to assign each of these regulations to a specific group, or cluster, based on the remark made by the flow manager, where each cluster represents a particular kind of weather disruption. These clusters then act as labels for the dataset, and each regulation is combined with the weather conditions observed during its implementation. This labelled dataset is then used to train a tree-based classifier using supervised learning. This two-step methodology enables the identification of the most likely precise trigger for each regulation, such as low visibility, snow, strong winds, etc. based solely on observed weather conditions. The clusters identified by the large language model are also compared with those discovered in previous research using self-learning and supervised clustering. Nevertheless, the practical applications of this method go far beyond the classification of weather-related regulations. This approach could be used in post-operational analysis to identify the primary triggers of any type of regulation - not just weather-related. Furthermore, it enables the analysis and classification of other types of text, such as notices to airmen, further broadening its potential use cases. This paper showcases the versatility and broad application of large language models in the field of air transportation.