Taxes collected by city halls are reverted towards common welfare; investments (such as infrastructure), and funding of public goods, as services on health, safety and education. The prediction of tax revenues is one of the tasks that have as challenges the city hall. This is an important task; because the information obtained from these predictions are important to support the city halls with relation the strategic planning. Thus, the investigation of prediction models designed for tax revenues through intelligent techniques is of great importance for public administration. One of the goals of this dissertation was to develop two models to prediction tax revenue using neural networks. The first model was designed considering endogenous variables only. The latter, considered both endogenous and exogenous variables. Another major challenge for city hall are irregularities in the taxes payment (error or fraud), which also affect the strategic planning. A monthly of all taxpayers is an impossible task to accomplish, due to the disproportion between the number of taxpayers and the reduced number of tax agents. Thus, research of methods based on intelligent techniques that indicate possible irregularities, is of great importance for tax agents. This way, another objective of this dissertation was to develop a model to identify possible suspects irregularities in the payment of the ISSQN (tax services of any nature). Prediction models were evaluated with three case studies using data from the city hall of Araruama. For the prediction model using endogenous variable, two case studies we used: (i) active debt revenues prediction, (ii) tax revenues prediction and (iii) ISSQN prediction, the latter using both endogenous and exogenous variables. In spite of the data used in the case studies, the results obtained from modeling are promising. Regarding tax irregularities, even though is not possible to evaluate the obtained results, the developed tool may be used as an indicator for future applications.