Cancer remains one of the leading causes of mortality worldwide, where early detection significantly improves patient outcomes and reduces treatment burden. This study investigates the application of Machine Learning (ML) techniques to predict cancer risk based on a combination of genetic and lifestyle factors. A structured dataset of 1,200 patient records was used, comprising features such as age, gender, Body Mass Index (BMI), smoking status, alcohol intake, physical activity, genetic risk level, and personal history of cancer. A full end-to-end ML pipeline was implemented, encompassing data exploration, preprocessing, feature scaling, model training, and evaluation using stratified cross-validation and a separate test set. Nine supervised learning algorithms were evaluated and compared, including Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machines (SVMs), and several ensemble methods. Among these, Categorical Boosting (CatBoost) achieved the highest predictive performance, with a test accuracy of 98.75% and an F1-score of 0.9820, outperforming both traditional and other advanced models. Feature importance analysis confirmed the strong influence of cancer history, genetic risk, and smoking status on prediction outcomes. The findings highlight the effectiveness of boosting-based ensemble models in capturing complex interactions within health data and support their potential use in personalized cancer risk assessment. This research underscores the value of integrating genetic and modifiable lifestyle variables into predictive modeling to enhance early detection and preventive healthcare strategies.