This paper introduces a novel, non-invasive tool for building a holistic neurological-hematological diagnostic framework that can be used for early detection of infection and stroke. We proposed and developed a multimodal sensing approach, where an ear canal–based acoustic system can be used to identify brain activities and a smartphone-based facial video analysis platform to estimate white blood cell (WBC) and hemoglobin (Hb) levels. Our ear-based EEG methodology achieved a remarkable 96% classification accuracy from the low-frequency level (>30hz), with regression models with mean R2scores above 0.96 across all EEG bands. For hematological diagnostics, the system predicted WBC counts with a mean squared error (MSE) of 0.79 and Hb levels with a mean absolute percentage error (MAPE) of 8.24% using optimized support vector regression. These results demonstrate the feasibility of real-time, point-of-care physiological in-clinic, or remote monitoring without an invasive approach. Future work will focus on motion-artifact mitigation and large-scale validation. By bridging neurology and hematology through accessible AI-driven technologies, this work lays the foundation for next-generation mHealth-based non-invasive diagnostic tools.