Lung cancer continues to remain a prominent issue in global health, with the identification of cases in the early stages being of utmost importance in enhancing the efficacy of treatment and increasing overall rates of survival. The implementation of Machine Learning (ML), due to its capacity to analyze extensive quantities of data and reveal complex patterns, is increasingly recognized as a valuable tool in the field of medical modeling for prediction. This study aims to examine the application of ML methodologies to forecast lung cancer. The objective is to create robust prediction models that can accurately identify individuals at a heightened risk of developing lung cancer by integrating clinical records, radiological imaging, and molecular markers. In this study, we employ Support Vector Machines (SVM), Decision Tree (DT), Multilayer Perceptrons (MLP), and Convolutional Neural Networks (CNN) as computational tools for the examination of lung cancer data. Feature selection is a procedure that serves to improve the performance and interpretability of models, as well as reduce the dimensionality of data. The high accuracy and sensitivity of our prediction algorithms in the detection of lung cancer risk have been substantiated through rigorous cross-validation and performance evaluation. This significant breakthrough presents novel prospects for the timely identification and tailored management of lung cancer. The employment of our ML methodology assists medical practitioners in the selection of suitable screening and monitoring protocols through the precise identification of patients at a heightened risk. After analyzing the performance of four different models, we found that the CNN had the best accuracy at 99%. This demonstrates the excellent predictive power of CNN for lung cancer, which holds great promise for enhancing both early identification and individualized treatment for people with lung cancer.