Early detection of cancers, especially in the mouth, throat, and lungs, significantly improves patient survival rates. This paper presents a comprehensive AI-driven approach combining various machine learning (ML) and deep learning (DL) techniques such as Fast Region-based Convolutional Neural Networks (FRCNN), Random Forest (RF), Support Vector Machines (SVM), and Logistic and Linear Regression models to enhance cancer detection capabilities. Each model's strengths are leveraged to create a hybrid system that excels in detecting and classifying cancerous regions in medical images and analyzing patient data. The proposed workflow incorporates automated image analysis, feature selection, classification, and probabilistic risk estimation, enhancing diagnostic accuracy while addressing challenges like data availability, model interpretability, and computational requirements. This integrated AI-based approach demonstrates potential for real-time clinical application and personalized cancer diagnostics.