The rapid increase in the number of vehicles is a major challenge for urban mobility worldwide. This exponential growth has led to the saturation of road infrastructure, creating congestion and efficiency problems in many cities. The consequences of this expansion of car fleets are felt in longer journey times, deteriorating air quality, and increasing pressure on public transportation systems. In this paper, we propose an innovative solution to improve the public transportation experience in developing countries by introducing intelligent bus stops. These advanced stops aim to improve citizens’ quality of life by offering real-time information on bus schedules and creating a more comfortable waiting environment. Additionally, the proposed solution aims to display alerts in intelligent bus stops in the event of disruptions such as accidents and traffic jams, enabling passengers to be informed in real time of changes, such as bus delays, and to maintain fluid mobility in the city. The results of our model, using a machine learning algorithm, show that real-time classification is effective, with a response time not exceeding 0.45 s. This performance ensures rapid updates of information, allowing passengers to adjust their travel plans according to traffic conditions. This contributes to improving the efficiency of the public transportation system and making it easier for citizens to get around, thus promoting the country’s economic and social development.