Since stroke disease often causes death or serious disability, active primary prevention and early detection of prognostic symptoms are very important. Stroke diseases can be divided into ischemic stroke and hemorrhagic stroke, and they should be minimized by emergency treatment such as thrombolytic or coagulant administration according to type. First, it is essential to detect in real time the precursor symptoms of stroke, which occur differently for each individual, and to provide professional treatment by a medical institution within the proper treatment window. However, prior studies have focused on developing acute treatment or clinical treatment guidelines after the onset of stroke rather than detecting the prognostic symptoms of stroke. In particular, recent studies have mostly used image analysis such as Magnetic Resonance Imaging (MRI) or Computed Tomography (CT) to detect and predict prognostic symptoms in stroke patients. Not only are these methodologies difficult to apply early in real time, but they also have limitations in terms of long testing times and high costs. In this paper, we propose a system that can predict and semantically interpret stroke prognostic symptoms based on machine learning using multimodal biosignals from Electrocardiogram (ECG) and Photoplethysmogram (PPG). As a result, the real-time prediction of stroke prognosis in elderly patients showed simultaneously high prediction accuracy and performance. Additionally, the CNN-LSTM model using raw data of ECG and PPG demonstrated a satisfactory prediction accuracy of 99.15%.