The growing demand for rapid, accurate, and accessible cancer screening solutions has driven the development of an Internet of Medical Things (IoMT)-enabled breast cancer diagnostic system powered by advanced deep learning techniques. This work introduces a compact, lightweight platform that combines an ultra-sensitive micro-bio-heat sensor array, arranged in a 3 × 3 configuration via Altium design, with an intelligent Convolutional Neural Network (CNN)–based classification pipeline. The embedded hardware unit integrates seamlessly with IoT infrastructure, enabling real-time data acquisition, cloud-based storage, and remote medical consultation through smartphone connectivity. To enhance classification performance, both standard CNN models and transfer learning with a pre-trained VGG16 network were explored for histopathological image analysis. The system was deployed on a Raspberry Pi, demonstrating efficient operation on resource-constrained hardware while maintaining high diagnostic capability. Evaluation on the BreakHis dataset yielded a classification accuracy of 82%, underscoring the viability of this approach for early detection and screening in rural and low-resource healthcare settings. The proposed framework highlights the potential of IoMT-driven AI solutions in transforming medical diagnostics through portability, affordability, and scalability.