Pavement performance models are a handy tool for road management agencies, allowing them to estimate pavement condition and designate the optimal corrective measure for road maintenance. Recent research has presented favorable results when using machine and deep learning techniques in pavement performance modeling. This research proposes a methodology for developing first-phase performance models for urban pavements, managed at the network level, for short-term condition prediction using machine and deep learning techniques in their construction. Specifically, random forest regression (RFR), support vector regression (SVR), gradient boosting regression (GBR), artificial neural networks (ANN), and recurrent neural networks (RNN) are used, and models are built to predict the Chilean urban pavement condition index (UPCI) with these algorithms, using a synthetic database developed in-house for the simulations. As for the obtained results, the iterations offer favorable results for short-term prediction, getting low average prediction errors (0.4% for the GBR algorithm) and RMSE results close to zero (0.063 for the GBR algorithm), which is relevant for each of the alternatives mentioned above, as it places them as recommendable tools for predicting urban pavement performance.