The differentiation between benign and malignant tumors remains a critical challenge in medical imaging and diagnostic radiology. Despite advancements in imaging modalities such as MRI, CT, PET, and ultrasound, diagnostic ambiguity persists due to overlapping visual and textural features between tumor types. This paper presents a theoretical framework for the development of a multimodal deep learning model capable of integrating heterogeneous medical imaging data and clinical metadata to improve diagnostic accuracy. The proposed architecture leverages convolutional and transformer-based feature extractors to jointly learn spatial, contextual, and semantic relationships across imaging modalities. This research aims to establish a theoretical foundation for a unified deep learning model that enhances interpretability, reliability, and clinical applicability in tumor classification.