Lung cancer is one of the most fatal types of cancer, therefore, the early and accurate diagnosis can greatly improve the patients’ quality of life, as well as the survival rate. In this research, the progress and the use of different forms of ML, sophistications such as deep learning and ensemble methods, and Support Vector Machines: Early Detection of Lung Cancer using Integrated Medical Imaging Data from CT and x-ray, Clinical Data, and Demographics. These models’ performances have therefore been evaluated by considering factors like accuracy, precision, recall, and even the AUC-ROC with regard to the ability of set apart between the malignant and the benign. The results presented in this work prove the significance of rich annotations and feature selection as an essential step in the improvement of model’s interpretability and accuracy. There are supposed effects of integrating the prediction models into the clinical processes as a way to diminishing lung cancer death rates, facilitated by improved methods of early detection and treatment planning. However, before such models are used in day to day practice, the concept requires more validation and research, and also recognition from the relevant authority. It is considered as a part of emerging literature proving the applicability of the ML in oncology and advocating for increased research and joint work of physicians and data scientists to enhance the lung cancer prediction models.