Urban Heat Island Effect (UHI) is a current and growing problem, present in many cities around the globe. It’s defined as the increase in temperature from the rural to urban gradient. Caused mainly by of changes in land cover, urban morphology, lack of vegetation and urban metabolism. UHI has been extensively explored through traditional statistical methods, due to the impacts it has on urban life and human health. Also, the threat it poses to the future of cities in climate change scenarios. This study proposes a different approach, via Artificial Neural Networks(ANNs) and Artificial Intelligence. ANNs are widely recognized in other fields for their ability to predict, recognize patterns and depict non-linear relationships. In ecology, ANNs are extensively recommended because of their ability to work with outliers, uncertainty and open systems; yet are seldomly used, due their notorious ‘black box’ reputation. ANNs main advantage is the ability to change their behaviour based on external information, that flows through the ANNs during the training phase. This study considers different variables constantly related to UHI, and evaluates the relative importance and the synaptic weights given to them by the ANN. It also evaluates the ability of the ANN to predict climate and its ability to project climate for future scenarios. Finally, it poses a question to whether they should be used as a tool in ecological studies. Aside from variables highly related to UHI like: precipitation, building height, vegetation areas; CO2 emissions was one of the variables with the highest relative importance. Prediction results, despite representing increasing amounts of variables and variation, were adjusted to the mean of the maximum temperature; and projections had a higher determination coefficient despite having less data as input. ANNs results need to be further transformed to be interpreted, they don’t explicitly state relationships between variables and have too many options to represent the system being considered. In spite and in favour, ANNs are an excellent tool to explore ecological phenomena, that should be very thoroughly explored.