In recent years, digitalization and computer-based technologies have greatly revolutionized the field of bioinformatics. Advance research and development of computer-based programs have enhanced various DNA sequencing technologies. This advancement has significantly broadened our understanding of genomic evolution and has widely contributed to the application of clinical genomics. Cancer has been one of the major causes of death across the world. Cancer is mainly caused due to the damage or changes in DNA that affect the function of genes which contain a set of instructions that control various functions of cells. This damage in genes that maintain DNA repair mechanism may lead towards genome instability allowing rapid growth of cancer.   Microsatellite instability (MSI) is one such condition characterized due to genomic alteration leading towards the failure of DNA repair mechanism in cancerous cells. MSI is found in various types of cancer but is most often found in colorectal cancer, gastric cancer, and endometrial cancer. Hence, detection of this MSI can greatly contribute towards cancer therapies and enable to plan for the best treatment. This study mainly focuses on evaluating the performance of MSI calling algorithms using targeted sequencing methods.   The literature provides a detailed outline of various topics related to MSI detection. Moreover, different computational methods like MSIsensor, MSIsensor-ct, MSIsensor-pro, MSings, MiMSI, and MSIsensor2 were used in this study for the detection of MSI in selected samples are thoroughly discussed in the methodology section. Finally, the findings of this study conclude that the MSI calling algorithms mentioned above provide accurate detection of MSI in the chosen samples. Also, these algorithms enable us to determine the MSI status of the chosen samples more precisely