This thesis presents a data-driven framework for predicting the deterioration of road infrastructure, with a focus on modeling the progression of alligator cracking in terms of area and severity. This research effort brings together multiple sources of data, covering but not limited to climate, traffic patterns, and visual imagery of pavement, and makes use of transfer learning to adapt models based on the U.S. Long-Term Pavement Performance (LTPP) dataset to the context of Italy, where historical data is limited. The study was completed as part of a collaboration with LOKI s.r.l. through the Asfalto Sicuro project, and aims to characterize and automate the detection and monitoring of major pavement distresses using Artificial Intelligence (AI), space-based data, and on-vehicle sensors.