The growing volume of waste worldwide is leading to challenges in pollution, prompting the need for innovative strategies, such as employing artificial intelligence to improve the waste management. The vehicle routing problem (VRP) is one of the best-known combinatorial optimization problems, it consists of searching for the best routes for a fleet of vehicles leaving a depot in order to visit a set of customers by meeting a set of constraints such as total transport cost, vehicle capacity and delivery times. This problem remains a pivotal challenge in optimizing the solid waste management within smart environments. Leveraging Artificial Intelligence (AI), particularly machine learning and reinforcement learning, enhances VRP solutions by enabling adaptive route planning and real-time decision-making. The Internet of Things (IoT) devices and sensors provide continuous data streams, allowing AI models to dynamically adjust routes based on traffic conditions, weather patterns, and delivery constraints. This integration facilitates the development of predictive algorithms that minimize transportation costs and reduce carbon footprints. This work will explore the application of artificial intelligence to optimize the vehicle routing problem in solid waste management by integrating machine learning algorithms and predictive models, we aim to enhance collection efficiency, reduce operational costs, and minimize environmental impact.