In this thesis, the concept of process mining and the advantages it provides in business is investigated. Process mining encompasses a wide range of techniques, including process discovery, conformance checking, forecasting, and bottleneck analysis. The goal is to provide better insight into operational processes, enabling organizations to manage workflows more effectively. As part of this thesis, the ProM and Disco tools were analyzed, which proved to be crucial for the implementation of process mining. Based on real examples, the use of these tools in different business contexts was demonstrated, with a special focus on blockchain applications. In addition, the possibilities of applying process mining in different domains are presented. Examples of the application of large language models (LLMs) in business process modeling are discussed, where the tool Nala2BPMN, developed for the automated generation of BPMN models from textual descriptions, showed high efficiency compared to traditional methods. Also, it was pointed out that the quality of the generated process models depends on the clarity of the textual descriptions and the precision of the query formulation, which emphasizes the importance of a structured approach in using LLMs for this purpose. In addition, examples from the field of blockchain applications where a thorough analysis of selected business processes was carried out were processed in detail. In conclusion, this thesis highlights the importance of integrating modern technologies, such as large language models and tools for process mining, with data from the blockchain system, as a key approach to improving efficiency and optimizing business processes.