In the domain of cancer prediction using pathology of computation. Starting with automating routine diagnostic procedures and algorithms for the analysis of histopathology images has been notable been noteworthy. Artificial intelligence, or AI, has advanced, with applications ranging from finding prognostic and predictive indicators derived from tissue structure to automating routine diagnostic tasks. A number of obstacles, including ethical, operational, technical, cultural, financial, and regulatory elements and dimensions, stand in the way of the integration of computational pathology into clinical settings, despite its enormous promise. This survey outlines the present state of translational medicine from the perspective of pathologists investigation, assessing clinical application, and tackling common issues impeding broad clinical use. Modern approaches to help with the use of computational pathology methods are also covered in the survey. This paper examines how digital technologies are changing pathology, with a particular emphasis on cancer detection prediction models. With the integration of machine learning (ML) and artificial intelligence (AI) tools, pathology has entered a new era of greater efficiency and accuracy in cancer diagnosis. Digital pathology slide digitization is made possible by the use of whole-slide imaging (WSI).