Deep learning (DL) has emerged as a transformative technology in oncology, offering innovative solutions for cancer detection, prognosis, and personalized treatment. Artificial intelligence (AI)-driven methodologies, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based models, have demonstrated remarkable potential in analyzing medical imaging, genomic data, and electronic health records (EHRs). These advancements have significantly improved early cancer detection, tumor classification, and treatment planning by providing automated, high-precision insights. However, despite these breakthroughs, challenges remain in data availability, model interpretability, ethical concerns, and clinical integration. The need for large, diverse, and high-quality datasets poses a significant barrier, while the black-box nature of many DL models raises concerns about trust and adoption among clinicians. Additionally, regulatory compliance and infrastructure limitations further complicate AI deployment in oncology. This chapter highlights the importance of interdisciplinary collaboration among AI researchers, oncologists, bioinformaticians, and policymakers to develop explainable and clinically validated AI models. Future research should focus on integrating multimodal AI approaches, leveraging federated learning for data privacy, and developing AI-driven personalized treatment strategies. Addressing these challenges will enable deep learning to revolutionize oncology, enhancing healthcare delivery, optimizing patient management, and ultimately improving survival rates.