PathoVision is an AI-powered framework that enhances cancer diagnosis by integrating histopathology images with clinical text using multimodal learning. Unlike traditional systems focused solely on image analysis, it employs the PLIP model to generate combined visual-text embeddings for more accurate and contextual understanding. A custom decoder processes these embeddings to classify cancer subtypes, while Grad-CAM provides interpretable heatmaps, ensuring transparency in decision-making. Designed for use in clinical diagnostics, research, and education, PathoVision addresses the limitations of manual pathology—such as subjectivity and time constraints—by offering a reliable, interpretable, and accessible solution. This framework represents a significant advancement in computational pathology, blending modern AI with traditional diagnostics to support expert pathologists and improve patient outcomes.