Deep learning in radiotherapy is an emerging research area that focuses on improving the planning, delivery, and outcome prediction of radiation treatment for cancer patients. Early approaches applied convolutional neural networks (CNNs) for organ and tumor segmentation in medical images, while subsequent research leveraged U-Net variants, attention mechanisms, and 3D-CNNs for precise delineation of targets and critical structures. Recent studies explore deep learning-based dose prediction, treatment plan optimization, image registration, adaptive radiotherapy, and outcome modeling using multimodal data (CT, MRI, PET) combined with clinical parameters. Applications span automated contouring, plan quality assessment, toxicity prediction, and personalized treatment planning, enhancing efficiency, accuracy, and patient safety. Current research also investigates transfer learning, domain adaptation, generative models for data augmentation, and explainable AI to facilitate clinical adoption, establishing deep learning as a transformative tool for advancing radiotherapy in precision oncology.