Lung cancer is one of the most prevalent and deadly diseases worldwide, primarily due to late-stage diagnosis and the limitations of conventional detection methods. Early and accurate identification is crucial for improving patient survival rates. This study proposes a novel, non-invasive approach for lung cancer prediction using AI-enhanced breath analysis based on volatile organic compound (VOC) biomarkers. The proposed system utilizes sensor-based breath data to capture VOC patterns associated with lung cancer. Advanced Preprocessing and feature selection techniques are applied to identify the most relevant biomarkers, reducing data complexity and improving model efficiency. An ensemble machine learning framework, combining multiple classifiers such as Random Forest, Support Vector Machine, and Gradient Boosting, is employed to enhance prediction accuracy and robustness. Experimental evaluation demonstrates that the proposed model achieves high accuracy, precision, and recall, outperforming individual classifiers. The system also shows strong potential for real-time implementation due to its computational efficiency. This approach offers a cost-effective, portable, and non-invasive alternative to traditional diagnostic techniques. Overall, the integration of VOC biomarker analysis with feature-selected ensemble learning provides a promising solution for early lung cancer detection, paving the way for improved screening methods and better clinical outcomes.