The human brain needs fresh oxygen from the blood to work properly. A brain stroke happens when blood flow to the brain is blocked. Identifying brain strokes is one of the vital areas of medical research performed with medical imaging techniques. There exist popular medical imaging techniques such as X-ray, computed tomography (CT), positron emission tomography (PET), magnetic resonance imaging (MRI), and functional magnetic resonance imaging (fMRI) for stroke detection. Existing studies related to stroke detection are mainly based on algorithmic upgradation of different imaging techniques. Also, some of the review papers focused on the hardware and perfusion level upgradation of imaging methods separately. However, a fusion of the imaging techniques with hardware/perfusion has not been explored yet. To overcome these gaps, in this review, we deeply discuss various imaging techniques for stroke detection over time. In addition, this review also highlights the fusion in categorical-medical imaging techniques based on different imaging algorithms, imaging devices, and physio-chemical (perfusion) aspects. This fusion in algorithm, device, and physio-chemical levels provides good achievement concerning the segmentation of stroke region. The outcome of our review (in terms of fusion) is illustrated in a categorical tree format, which provides significant help to the interested researcher for accurate guidance.