Rapid advancements in machine learning (ML) have significantly impacted the field of medical imaging, particularly in tumor prediction and analysis. This paper aims to provide a comprehensive review of the application of advanced ML techniques in tumor prediction, highlighting existing knowledge gaps and exploring new implications. By examining a range of studies utilizing convolutional neural networks (CNNs), U-Net architectures, and other deep learning models, we illustrate the methodologies and their effectiveness in tumor classification and segmentation. Key findings indicate that models such as Multi-ResNet and EfficientNet achieve high accuracy and recall, surpassing traditional methods. The use of data augmentation, transfer learning, and ensemble techniques further enhances model performance. Despite these advancements, challenges such as data heterogeneity, model interpretability, and clinical integration remain. This review underscores the potential of ML to revolutionize tumor diagnosis and treatment, advocating for ongoing research to address remaining gaps and translate these technologies into clinical practice.