Objectives: Explore the potential of ensemble learning techniques like Bagging Tree, Random Forest, and Ensemble Extra Tree in transforming mesothelioma diagnosis.Overcome challenges associated with late-stage detection and limited treatment options using advanced machine learning algorithms.Enhance predictive power and feature extraction capabilities through the combination of diverse ensemble algorithms. Methods:Utilize Bagging Tree, Random Forest, and Ensemble Extra Tree algorithms to analyze extensive data sources including clinical records, imaging scans, and biomarker profiles.Construct a diverse ensemble model to improve accuracy in distinguishing mesothelioma from other thoracic diseases.Conduct rigorous experimentation to validate the performance and interpretability of the ensemble approach. Findings: The ensemble approach exhibits unparalleled accuracy in mesothelioma classification, offering potential for early intervention and personalized treatment strategies.The model's interpretability provides valuable insights for clinicians, bridging the gap between artificial intelligence and human expertise.The integration of advanced machine learning tools into clinical practice can lead to enhanced patient outcomes in mesothelioma management. Novelty: This research marks a significant shift by leveraging ensemble learning techniques to revolutionize mesothelioma diagnosis.The study showcases the transformative capabilities of machine learning in overcoming longstanding challenges in mesothelioma management. The interpretability of the ensemble model fosters trust and seamless integration of cutting-edge tools into clinical practice, paving the way for improved patient care.