The Ebola epidemic that took place in 2012 was the deadliest in modern history. Our research looks at the disease that is widespread in Kenya, taking into consideration potential triggers. Machine learning is used to forecast the outcomes of the study. It should be seen that there are a range of steps that can be achieved in the real world that can affect the rates of illness and pollution like such things as geographic lockdowns, segregation, monitoring, quarantine, foreign travel restrictions, collective exchange of surveillance progress and by putting in wide scale social initiatives.It must also be noted that most mathematical models are only as good as the data that they were built on. In order to provide broad audiences in the research world with an understanding of viral spreading patterns, a short introduction about biological systems models and the python source codes of simulation are also provided.