Traffic congestion has many negative effects, as this problem increases the rate of air pollution, which in turn affects the health of the individual and the climate. Congestion is a global phenomenon, not regional or local, and it means that traffic flow on the roads does not flow at the usual rates, which impedes the access of road users to the intended places at the possible times. This research thesis intends to achieve an experimental and explanatory research questions which have been formulated and solved carefully to generate a classification model, resulting in generating significant recommendations that could minimize the traffic congestion problem in sultanate of Oman. This research work attempts to do analysing, classifying and visualizing, understanding the situation of the traffic congestion in Sultanate of Oman. The data analysis process and the mixed methodology of quantitative and qualitative approaches supported by the exploratory analysis have been proposed. As a source of information, a database of accident records over several years was collected from ROP and investigated upon. In total, the traffic accident data obtainable in three data sets contains about 105,000 records describing the vehicle accident profiles. The survey data set contains 420 respondent cases and 23 questions presented as variables. Using several techniques and filtering methods, this work answers all research questions in a systemic way based on reliable data analysis process. The experimental and knowledge flow available in Weka tools have been used to develop the proposed classifier model. ‘The Receiver Operating Characteristics (ROC) and the Precision-Recall Curve (PRC) plots have been used to measure the sensitivity of the classifier. This proposed work provides improved results in the classifier performance, where the performance is improved from 75% correct classifications to 95% correct classifications and with 1.0 Precision ,1.0 Recall , 1.0 F-Measure ,1.0 ROC Area and 0.99 PRC Area . The J48 classifier performs better than the other selected classifiers and was used for building the classifier model to create the tree structure for generating a set of rules and recommendations. The results and conclusions made in this work could be useful for improving road safety, driving license management system for training and testing conducted by ROP. The results can be also useful for insurance companies for planning to introduce life and health insurance programs for drivers and travelers.