Lung cancer is one of the main cause of cancer related death globally so the early detection techniques are really essential for reducing the deaths globally every year. In order to diagnose lung cancer, our research proposes a hybrid machine learning technique which uses XGBoost for tabular data analysis and Convolutional Neural Networks (CNN) for histopathological image classification. The suggested approach makes use of two datasets: a histopathological image dataset with 25,000 images of lung cancer, and a lung cancer dataset with 15 clinical and demographic variables. While the CNN model showed 91.06% training and validation accuracy for image classification whereas the XGBoost model achieved 98.39% accuracy for tabular data set. we used Streamlit to create a web based tool for this research project for real time prediction. The hybrid approach is great for early lung cancer screening programs and early lung cancer detection and support systems.