Lung cancer, which is the leading cause of cancer-related death worldwide and is characterized by genetic changes and heterogeneity, presents a significant treatment challenge. Existing approaches utilizing Machine Learning (ML) techniques for identifying driver modules lack specificity, particularly for lung cancer. This study addresses this limitation by proposing a novel method that combines gene-gene interaction network construction with ML-based clustering to identify lung cancer-specific driver modules. The methodology involves mapping biological processes to genes and constructing a weighted gene-gene interaction network to identify correlations within gene clusters. A clustering algorithm is then applied to identify potential cancer-driver modules, focusing on biologically relevant modules that contribute to lung cancer development. The results highlight the effectiveness and robustness of the clustering approach, identifying 110 unique clusters ranging in size from 4 to 10. These clusters surpass evaluation requirements and demonstrate significant relevance to critical cancer-related pathways. The identified driver modules hold promise for influencing future approaches to lung cancer diagnosis, prognosis, and treatment. This research expands our understanding of lung cancer and sets the stage for further investigations and potential clinical advancements.