Urban Crime is one of the major problems encountered in any society in the world. Currently controlling violent crime is an urgent need for security agents and agencies to battle and eradicate crime in cities. The aim of the study is to develop a model for characterization and pattern development for the status of violent crime using data mining technique. This study followed knowledge Discovery process to achieve the goal of building a predictive model using secondary data from south wollo zone Dessie city criminal court taking 8600 instances that are recorded from 2005E.C up to 2010 E.C. The purpose of this study was to explore the applicability of data mining technique in the efforts of urban crime characterization and pattern development with particular emphasis to the Dessie city. WEKA 3.9.3 data mining tools and classification techniques such as J48 decision tree, Naïve Bayes, Bayes Net and REP Tree and PART rule induction algorithms were employed as means to address the research problem. Model comparison is done based on TP (sensitivity) and FP (specificity) rates, precision, recall, F-measure, ROC area and accuracy. After applying N-fold cross validation test option 7-fold is used to check the performances of each classifier. In this particular study, the predictive model developed using J48 pruned with all attributes perform better in predicting violent crime cases with an accuracy of 94.46%. Generally the results from this study were encouraging and confirmed that applying data mining techniques could indeed support a predictive model building task that predicts as indicator of the status of violent crime in Ethiopia. Thus, the outcome of this study helps police department and any concerned body as policy makers to design a proper and suitable behavioral characterization for the purpose of preventive and control program to combat crime. For the future, by integrating large criminal dataset from different cities by employing other classification algorithms, tools and techniques could yield better results.