Rape is serious crimes that have a troubling impact on victims in particular and the society in general. It is very dynamic and flexible. It is also sensitive for social and political crises. Therefore, developing effective rape crime prevention and mitigation methods to protect the people is indispensable. In relation to these, data mining techniques play a critical role to develop predictive models that can identify individuals who are at risk of being raped. The trend data modeling and analysis is essential to interfere and prevent the victim from various suspected rapes. In this thesis, a predictive model for rape prediction and prevention has developed using data mining techniques. The K-nearest neighbors (KNN) and decision tree (DT) algorithms have been used to develop the model. The data obtained from Addis Ababa Police Commission (AAPC) that has been collected from 11 sub-cities of Addis Ababa City. The data has organized and prepared in a table form of 10 columns and 4067 rows. The data has been pre-processed and trained so as to develop the predictive model. After the models have been developed, its performance has been evaluated using testing data set and accuracy. The KNN and DT models were able to predict the probability of a rape crime occurring with an accuracy of 93.5% and 88.1% respectively. Based on the result of model comparison score, KNN was more effective technique than DT. The results show that the system is able to accurately predict the rape crime in a given area. The models can be used to identify areas where prevention efforts should be focused and hence to design appropriate interventions mechanisms to prevent rape crime. Key Words: Rape Crime; Data Mining; K-nearest neighbors (KNN) and Decision Tree (DT).