One essential medical imaging method is magnetic resonance imaging (MRI), which is well known for providing sharp, high-resolution images of the human body with exceptional soft tissue contrast. This makes it possible for medical experts to learn important information about the morphology, structural integrity, and physiological functions of the human body. Although quantitative imaging is now confined to low spatial resolutions or takes a long time to scan, it gives compositional measures of the human body. Compressed sensing (CS) and deep learning (DL) reconstructions have helped to attenuate the associated under sampling artifacts, whereas under sampled k-space data acquisitions have greatly contributed to a shorter MRI scan time. On the other hand, magnetic resonance fingerprinting (MRF) offers a quick and flexible framework for simultaneously acquiring and measuring several tissue characteristics from an MRI scan. The design of pulse sequences, quick (under sampled) data capture, encoding of tissue attributes in MR signal evolutions or fingerprints, and simultaneous recovery of numerous quantitative spatial maps are the four main components of the MRF framework. This research addresses the trends related to these four important components of the MRF framework by conducting a thorough literature assessment. All body sections and all magnetic field strengths have unique obstacles in MRF, which may offer prospects for additional research. Our goal is to examine the best practices for several MRF applications, including musculoskeletal, cardiac, and brain imaging, as well as for each of the major MRF facets. We will be able to evaluate potential future trends and how they will affect the integration of MRF into these biomedical imaging applications by thoroughly reviewing these applications.