Cancer can be described as the abnormal growth of tissues in the body. These cancer cells can also be defined as malignant cells. Different treatments of these tumors have been developed over the years. The type of treatments applied is pre-determined by the location, the type, and the severity of the tumors. The person’s health status also plays a vital role in pre-determine the method preferred for the treatment. Calculus has also been used in cancer treatment and has helped health care providers locate the affected cells. Thus, one can analyze the different calculus applications used by physicians in cancer treatment and monitoring and their effectiveness. Chemotherapy treatment is one of the common approaches used by many doctors when treating cancer patients. Chemotherapy entails using drugs to treat the disease, and calculus is applied to monitor cancer cells. In cancer disease, chemotherapy works by destroying the affected cells and limiting their spread (Crawford et al., 2019). The mathematical concept of calculus is applied when doctors consider the diffusion of different drugs into the bloodstream and inside the cancer cells. Calculus plays the most prominent role in research and specifically in predicting and monitoring the impact of chemotherapy on cancer tumors by using the Fractional Diffusion Equation (FDE) (Jenner et al., 2018). This methodology is applied not only during the analysis of the impacts of any particular drug but also in different cancers. Various researchers use different kinds of equations to determine the chemotherapy method’s effect on the treatment and management of cancer. For instance, Pilli’s designed an approach based on an ordinary differential equation system (Victori & Buffa, 2019). This system was helpful during the analysis of the normal growth of cancer. The system constantly analyzed the population of the cells and their levels after chemotherapy has been conducted. However, the method has several successes but also has various challenges. One issue is that the system cannot analyze the effects of interleukin-2 on any growing tumor (Victori & Buffa, 2019). Due to this setback of the method, the founder, Pilli’s, was forced to develop a new model that could also consider interleukin 2 (Victori & Buffa, 2019). Therefore, the newly developed model allowed the production of endogenous interleukin-2 while this IL-2 led to the stimulation of natural killer cells thriving and proliferation (Victori & Buffa, 2019). This system always considered a treatment of the cancer cells that were growing during the tumor’s treatment process. In some instances, many researchers focus on mathematical modeling for the diffusion of the anti-cancer drugs than the cancer tumor. Generally, this method depends on the diffusion of the drug applied for chemotherapy inside the tumor. When the drug comes closer to the cancerous cells, it diffuses into the tumor via the capillaries, surrounded by the tumor (Sigal et al., 2018). Its distribution depends on how the drug was administered, consumed, and diffused. The drug will enter the tumor through diffusion, which tampers the cancer cells’ behaviors. All this arises from the fact that different drugs travel in different velocities when they are in the bloodstream. Hence, they have different diffusion rates