Efficient road maintenance is crucial for the socioeconomic development of a country. However, traditional pavement analysis systems have limitations in their adaptability to the current reality, which hinders the effective planning of pavement rehabilitation operations. In response to this need, this article presents two techniques to improve decision making in the optimization of road infrastructure solutions: heuristic optimization and reinforcement learning. Both techniques present weaknesses and strengths when applied to road network maintenance management. Heuristic optimization offers robust solutions, but its effectiveness is compromised in uncertainty scenarios. In contrast, reinforcement learning stands out for its ability to adapt to changing and uncertain environments, offering more dynamic and efficient planning, but has limitations in including constraints. This study underscores the importance of adopting innovative and adaptive approaches to road maintenance planning to ensure the long-term sustainability and efficiency of road infrastructures.