In contemporary corporate environments, the efficient handling of service desk tickets is pivotal for ensuring smooth IT operations. Support tickets, encompassing a range of customer requests and issues, are essential communication tools between customers and support teams. Proper categorization and swift resolution of these tickets are crucial tasks, ensuring customer satisfaction, productivity, compliance with service level agreements, and cost efficiency. In this study, the potential of Latent Dirichlet Allocation (LDA), an unsupervised machine learning algorithm, was explored for automatic categorization of service desk queries. Leveraging firsthand experience as an IT intern at Lavazza and access to a substantial dataset of tickets, this research successfully implemented LDA using Dariah-DE Topics Explorer.