Cancer is a complex disease with diverse genetic changes that pose significant treatment challenges due to its heterogeneity. Identifying driver modules, which are crucial for cancer progression, has been aided by artificial intelligence (AI) techniques. However, existing approaches lack specificity, particularly for cancer types like lung cancer. This thesis addresses this gap by proposing a method that combines a gene-gene interaction network construction with AI-based clustering to identify distinct driver modules specific to lung cancer. The research aims to enhance our understanding of the disease by leveraging publicly available databases and large datasets using design science methodology. By mapping biological processes to genes and constructing a weighted gene-gene interaction network, correlations within gene clusters are identified. A clustering algorithm is applied to derive potential cancer-driver modules and pinpoint biologically relevant modules that contribute to the development of lung cancer. The results demonstrate the effectiveness and robustness of the clustering approach, with 110 unique and non-overlapping clusters identified, ranging in size from 4 to 10. These clusters surpass the evaluation requirements and exhibit significant relevance to critical pathways. The findings challenge previous assumptions about gene clusters and their significance in lung cancer, providing insights into the molecular underpinnings of the disease. The identified driver modules hold promise for influencing future approaches to diagnosis, prognosis, and treatment in the management of lung cancer. By expanding our understanding of the disease, this research paves the way for further investigations and potential clinical advancements.