Early cancer detection remains one of the most critical challenges in modern healthcare, where delayed diagnosis significantly reduces survival outcomes. Recent advancements in artificial intelligence, particularly deep learning, have enabled transformative progress in medical imaging analysis. Deep learning-based computer vision models, such as convolutional neural networks (CNNs), transformers, and hybrid attention architectures, can automatically extract complex spatial, morphological, and temporal patterns from multimodal imaging data including MRI, CT, PET, mammography, histopathology, and ultrasound. These models surpass traditional radiological assessment by identifying subtle tissue abnormalities and tumor microenvironment variations invisible to the human eye. At a broader scale, the integration of multimodal imaging with radiogenomics linking quantitative imaging features with genomics, transcriptomics, and epigenetic biomarkers has introduced a new paradigm for personalized oncology. This radiogenomic fusion allows the prediction of tumor genotype, immune response, molecular subtypes, and treatment resistance without invasive biopsies. By incorporating multi-omics data and imaging biomarkers into deep learning frameworks, clinicians can generate patient-specific risk scores, detect early tumor onset, and forecast disease progression with high sensitivity and specificity. Narrowing down, this research explores deep learning-based computer vision models that fuse imaging and genetic data using architectures such as multi-stream CNNs, graph neural networks, and transformer-based radiogenomic encoders. These frameworks leverage feature-level and decision-level fusion to correlate radiomic phenotypes with genomic signatures, enabling early diagnosis of cancers such as glioblastoma, breast, lung, colorectal, and prostate cancers. Additionally, challenges including data heterogeneity, interpretability, limited annotated datasets, and ethical concerns surrounding genomic privacy are addressed. The study emphasizes the need for standardized imaging protocols, federated learning systems, and clinically validated AI pipelines to ensure accurate, reproducible, and globally deployable cancer detection systems.