Cancer prediction is a critical area of research that holds immense potential for improving early detection and treatment outcomes. In recent years, the blending of machine learning (ML) and deep learning (DL) approaches has emerged as a promising approach to enhance the accuracy and efficiency of cancer prediction models. This research paper provides an overview of the latest advancements in cancer prediction using a combination of ML and DL methodologies. We examine into the principles of ML and DL algorithms and their applications in cancer prediction and survival analysis. By harnessing the robust capabilities of machine learning (ML) and deep learning (DL), researchers have unlocked unprecedented insights into cancer biology, such as the identification of novel biomarkers, improved tumor classification, and more accurate survival predictions. Recent studies show that CNN-based models achieved an accuracy of 92% in breast cancer detection, outperforming traditional ML methods, which typically achieve accuracies around 80%. Furthermore, in this review, we highlight recent research papers that showcase the efficacy of top-performing ML and DL models, such as the use of ResNet and VGGNet for tumor classification, the application of U-Net for medical image segmentation, and the deployment of XGBoost in predicting patient survival outcomes, such as breast cancer, lung cancer, prostate cancer, and leukemia. Additionally, we examine the challenges and opportunities associated with integrating ML and DL approaches in cancer prediction, including data preprocessing, feature selection, model interpretability, and scalability. The continued evolution of these technologies holds the potential to revolutionize personalized medicine and improve patient outcomes.