Neural Networks has been a potential technological basis which is being approached within the healthcare system for increasing its overall efficiency. With the inculcation of the computational approach, the solving of problems related to patient care and diagnosis can be made easier. On the other hand, the prognostics methods involving Neural Networks and Deep Transfer learning Methods allows for an easier prediction of the health related aspects of the patients. Along with such, the development of innovative medicines through the examination of the occurring disease and recommendation of the treatments based on the medical history of the patients have also been achieved with the application of deep learning. Biochemical analysis and the determination of image analysis is also enabled with the inculcation of Neural Networks and Deep Transfer learning within the various other advantages. The study has shed light on the background of such computational technology within the healthcare system, and identified the different measures which are allowed to examine for thyroid cancer through such technologies. The determination of the features which are associated with Neural Networks and Deep Transfer learning for predicting and identifying thyroid cancer have also been interpreted within the study. Extraction of treatment measures from the digital cloud and the integration of potential measures for reaching the effective scenarios can also be enabled with the application of Neural Networks. Impregnation of layered algorithmic architecture for interpreting the test results, and determining the course of treatment is made feasible through Neural Networks. The study has also focused on such a notion, and helped in providing an elaborate overview of the various advantages seen through the inclusion of computational analysis for treating thyroid cancer.